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1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" 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1.75h-9.69l-3.573 3.573A1.458 1.458 0 0 1 5 21.043V18.5H3.25a1.75 1.75 0 0 1-1.75-1.75ZM3.25 4a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h2.5a.75.75 0 0 1 .75.75v3.19l3.72-3.72a.749.749 0 0 1 .53-.22h10a.25.25 0 0 0 .25-.25V4.25a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Review</div> Manage code changes </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"discussions","context":"product","tag":"link","label":"discussions_link_product_navbar"}" href="https://github.com/features/discussions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-comment-discussion color-fg-subtle mr-3"> <path d="M1.75 1h12.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 14.25 14H8.061l-2.574 2.573A1.458 1.458 0 0 1 3 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12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Solutions <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 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Link--secondary" data-analytics-event="{"location":"navbar","action":"view_all_industries","context":"solutions","tag":"link","label":"view_all_industries_link_solutions_navbar"}" href="/solutions/industry"> View all industries </a></li> </ul> </div> </div> <div class="HeaderMenu-trailing-link rounded-bottom-2 flex-shrink-0 mt-lg-4 px-lg-4 py-4 py-lg-3 f5 text-semibold"> <a href="/solutions"> View all solutions <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-right HeaderMenu-trailing-link-icon"> <path d="M6.22 3.22a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L9.94 8 6.22 4.28a.75.75 0 0 1 0-1.06Z"></path> </svg> </a> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Resources <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="resources-topics-heading">Topics</span> <ul 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data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"executive_insights","context":"resources","tag":"link","label":"executive_insights_link_resources_navbar"}" href="https://github.com/solutions/executive-insights"> Executive Insights </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Open Source <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"github_sponsors","context":"open_source","tag":"link","label":"github_sponsors_link_open_source_navbar"}" href="/sponsors"> <div> <div class="color-fg-default h4">GitHub Sponsors</div> Fund open source developers </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"the_readme_project","context":"open_source","tag":"link","label":"the_readme_project_link_open_source_navbar"}" href="https://github.com/readme"> <div> <div class="color-fg-default h4">The ReadME Project</div> GitHub community articles </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="open-source-repositories-heading">Repositories</span> <ul class="list-style-none f5" aria-labelledby="open-source-repositories-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"topics","context":"open_source","tag":"link","label":"topics_link_open_source_navbar"}" href="https://github.com/topics"> Topics </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"trending","context":"open_source","tag":"link","label":"trending_link_open_source_navbar"}" href="https://github.com/trending"> Trending </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" 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</div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button 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seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header Overlay-header--divided"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="custom-scopes-dialog-title"> Saved searches </h1> <h2 id="custom-scopes-dialog-description" class="Overlay-description">Use saved searches to filter your results more quickly</h2> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="custom-scopes-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="custom-scopes-dialog-title"> <div data-view-component="true" class="Overlay-body"> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <div hidden class="create-custom-scope-form" data-target="custom-scopes.createCustomScopeForm"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" 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class=\"heading-element\" dir=\"auto\"\u003eKnowledge Editing for LLMs Papers\u003c/h1\u003e\u003ca id=\"user-content-knowledge-editing-for-llms-papers\" class=\"anchor\" aria-label=\"Permalink: Knowledge Editing for LLMs Papers\" href=\"#knowledge-editing-for-llms-papers\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/zjunlp/ModelEditingPapers\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3418ba3754faddfb88c5cbdc94c31ad670fc693c8caa59bc2806c9836acc04e4/68747470733a2f2f617765736f6d652e72652f62616467652e737667\" alt=\"Awesome\" data-canonical-src=\"https://awesome.re/badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/28f4d479bf0a9b033b3a3b95ab2adc343da448a025b01aefdc0fbc7f0e169eb8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d677265656e2e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-green.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/108e193cfdc9a788229dfc1184064ec25065b5a3d5b064b842922faef004d198/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f7a6a756e6c702f4d6f64656c45646974696e675061706572733f636f6c6f723d677265656e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/108e193cfdc9a788229dfc1184064ec25065b5a3d5b064b842922faef004d198/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f7a6a756e6c702f4d6f64656c45646974696e675061706572733f636f6c6f723d677265656e\" alt=\"\" data-canonical-src=\"https://img.shields.io/github/last-commit/zjunlp/ModelEditingPapers?color=green\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/30c6078aaee2b242e7c07f16316804464f124b8041003f717280157f42a411c0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f5052732d57656c636f6d652d726564\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/30c6078aaee2b242e7c07f16316804464f124b8041003f717280157f42a411c0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f5052732d57656c636f6d652d726564\" alt=\"\" data-canonical-src=\"https://img.shields.io/badge/PRs-Welcome-red\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eMust-read papers on \u003ca href=\"https://arxiv.org/abs/2305.13172\" rel=\"nofollow\"\u003eknowledge editing\u003c/a\u003e for large language models.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🔔 News\u003c/h2\u003e\u003ca id=\"user-content--news\" class=\"anchor\" aria-label=\"Permalink: 🔔 News\" href=\"#-news\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNew Reports\u003c/strong\u003e\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003e\u003cem\u003eReport\u003c/em\u003e\u003c/th\u003e\n\u003cth align=\"center\"\u003eTopic\u003c/th\u003e\n\u003cth align=\"center\"\u003ePPT Resource\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eNLPCC2024 tutorial\u003c/td\u003e\n\u003ctd align=\"center\"\u003eKnowledge Mechanism, Fusion, Editing for LLMs\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://drive.google.com/file/d/1MCzcOhK89bD2-b3s25Tb9cqDh8dEhIGt/view?usp=sharing\" rel=\"nofollow\"\u003eGoogle Drive\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eInvited Talk\u003c/td\u003e\n\u003ctd align=\"center\"\u003eEditing Large Language Models Advancing Machine Understanding and Control\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://drive.google.com/file/d/19t7BiCsiZx2c_wPZCGbH8BEwe5YSkJOX/view?usp=sharing\" rel=\"nofollow\"\u003eGoogle Drive\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eCCL2024 tutorial\u003c/td\u003e\n\u003ctd align=\"center\"\u003e大语言模型知识机理、融合与编辑\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://drive.google.com/file/d/1Tp7pWAl1MqMvNQUplPS411WQmmAmodcB/view?usp=sharing\" rel=\"nofollow\"\u003eGoogle Drive\u003c/a\u003e \u0026amp; \u003ca href=\"https://pan.baidu.com/s/14IFludc3KROrCdvSYrOfAw?pwd=shja\" rel=\"nofollow\"\u003eBaiduPan\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eIJCAI2024 tutorial\u003c/td\u003e\n\u003ctd align=\"center\"\u003eKnowledge Editing for Large Language Models\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://drive.google.com/file/d/1sg3Dy3gQo2bwOoLwWMFh57NfHROf3Ahg/view?usp=sharing\" rel=\"nofollow\"\u003eGoogle Drive\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eCOLING2024 tutorial\u003c/td\u003e\n\u003ctd align=\"center\"\u003eKnowledge Editing for Large Language Models\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://drive.google.com/file/d/1vFzRYjnzkuZaNdjdIxQwWbEybCY7YqY9/view?usp=sharing\" rel=\"nofollow\"\u003eGoogle Drive\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e北京智源大会\u003c/td\u003e\n\u003ctd align=\"center\"\u003e大语言模型知识机理与编辑问题\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://pan.baidu.com/s/1X9n2LAkYYXFnPgkV_2b57Q?pwd=du6w\" rel=\"nofollow\"\u003eBaiduPan\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVALSE2024 tutorial\u003c/td\u003e\n\u003ctd align=\"center\"\u003eKnowledge Mechanism and Editing for Large Language Models\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://drive.google.com/file/d/19T-InKopH-VHKAtphy9M6H366dXnenQX/view?usp=sharing\" rel=\"nofollow\"\u003eGoogle Drive\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eAAAI2024 tutorial\u003c/td\u003e\n\u003ctd align=\"center\"\u003eKnowledge Editing for Large Language Models\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://drive.google.com/file/d/1fkTbVeRJSWmU7fBDeNf1OhHEkLSofQde/view?usp=sharing\" rel=\"nofollow\"\u003eGoogle Drive\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003e2024-09-26 Our paper: \"\u003ca href=\"https://arxiv.org/abs/2405.14768\" rel=\"nofollow\"\u003eWISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models\u003c/a\u003e\" and \"\u003ca href=\"https://arxiv.org/abs/2405.17969\" rel=\"nofollow\"\u003eKnowledge Circuits in Pretrained Transformers\u003c/a\u003e\" have been accepted by NeurIPS 2024. Our papers: \"\u003ca href=\"https://arxiv.org/abs/2407.15017\" rel=\"nofollow\"\u003eKnowledge Mechanisms in Large Language Models: A Survey and Perspective\u003c/a\u003e\" and \"\u003ca href=\"https://arxiv.org/abs/2403.06259\" rel=\"nofollow\"\u003eEditing Conceptual Knowledge for Large Language Models\u003c/a\u003e\" have been accepted by EMNLP 2024 Findings.\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e2024-07-22 We release a new paper: \"\u003ca href=\"https://arxiv.org/abs/2407.15017\" rel=\"nofollow\"\u003eKnowledge Mechanisms in Large Language Models: A Survey and Perspective\u003c/a\u003e\", which reviews how knowledge is acquired, utilized, and evolves in large language models.\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e2024-05-16 Our paper \"\u003ca href=\"https://arxiv.org/abs/2403.14472\" rel=\"nofollow\"\u003eDetoxifying Large Language Models via Knowledge Editing\u003c/a\u003e\" has been accepted by ACL 2024.\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e2024-01-03 We release a new paper:\"\u003ca href=\"https://arxiv.org/abs/2401.01286\" rel=\"nofollow\"\u003eA Comprehensive Study of Knowledge Editing for Large Language Models\u003c/a\u003e\" with a new benchmark \u003ca href=\"https://huggingface.co/datasets/zjunlp/KnowEdit\" rel=\"nofollow\"\u003eKnowEdit\u003c/a\u003e! We are looking forward to any comments or discussions on this topic\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e2024-12-09 Our paper \"\u003ca href=\"https://arxiv.org/abs/2301.10405\" rel=\"nofollow\"\u003eEditing Language Model-based Knowledge Graph Embeddings?\u003c/a\u003e\" has been accepted by AAAI 2024.\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e2023-11-18 We will provide a tutorial on \u003cem\u003eKnowledge Editing for Large Language Models\u003c/em\u003e at COLING 2024.\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e2023-10-25 We will provide a tutorial on \u003cem\u003eKnowledge Editing for Large Language Models\u003c/em\u003e at AAAI 2024.\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e2023-10-22 Our paper \"\u003ca href=\"https://arxiv.org/abs/2310.08475\" rel=\"nofollow\"\u003eCan We Edit Multimodal Large Language Models?\u003c/a\u003e\" has been accepted by EMNLP 2023.\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e2023-10-08 Our paper \"\u003ca href=\"https://arxiv.org/abs/2305.13172\" rel=\"nofollow\"\u003eEditing Large Language Models: Problems, Methods, and Opportunities\u003c/a\u003e\" has been accepted by EMNLP 2023.\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e2023-8-15 We release the paper \"\u003ca href=\"https://arxiv.org/abs/2308.07269\" rel=\"nofollow\"\u003eEasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models\u003c/a\u003e.\"\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e2023-07 We release \u003ca href=\"https://github.com/zjunlp/EasyEdit\"\u003eEasyEdit\u003c/a\u003e, an easy-to-use knowledge editing framework for LLMs.\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e2023-06 We will provide a tutorial on \u003cem\u003e\u003ca href=\"https://drive.google.com/file/d/1EW-cusC_llCM0wEshkIdYuYrvfBPCDRz/view?usp=sharing\" rel=\"nofollow\"\u003eEditing Large Language Models\u003c/a\u003e\u003c/em\u003e at AACL 2023.\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e2023-05 We release a new analysis paper:\"\u003ca href=\"https://arxiv.org/abs/2305.13172\" rel=\"nofollow\"\u003eEditing Large Language Models: Problems, Methods, and Opportunities\u003c/a\u003e\" based on this repository! We are looking forward to any comments or discussions on this topic :)\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e2022-12 We create this repository to maintain a paper list on \u003cem\u003eKnowledge Editing\u003c/em\u003e.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🔍 Contents\u003c/h2\u003e\u003ca id=\"user-content--contents\" class=\"anchor\" aria-label=\"Permalink: 🔍 Contents\" href=\"#-contents\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#-why-knowledge-editing\"\u003e🌟 Why Knowledge Editing?\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#keywords\"\u003eKeywords\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#comparisons-of-different-technologies\"\u003eComparisons of the different technologies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#-papers\"\u003e📜 Papers\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#overview\"\u003eOverview\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#methods\"\u003eMethods\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#preserve-parameters\"\u003ePreserve Parameters\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#memory-based\"\u003eMemory Based\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#additional-parameters\"\u003eAdditional Parameters\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#modify-parameters\"\u003eModify Parameters\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#finetuning\"\u003eFinetuning\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#meta-learning\"\u003eMeta-learning\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#locate-and-edit\"\u003eLocate and edit\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#more-related-papers\"\u003eMore Related Papers\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#analysis\"\u003eAnalysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#-resources\"\u003e🧰 Resources\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#benchmarks-and-tasks\"\u003eBenchmarks and Tasks\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#tools\"\u003eTools\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contribution\"\u003e🎉 Contribution\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#contributors\"\u003eContributors\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contributing-to-this-paper-list\"\u003eContributing to this paper list\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#acknowledgement\"\u003eAcknowledgement\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#-citation\"\u003e🚩Citation \u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🌟 Why Knowledge Editing?\u003c/h2\u003e\u003ca id=\"user-content--why-knowledge-editing\" class=\"anchor\" aria-label=\"Permalink: 🌟 Why Knowledge Editing?\" href=\"#-why-knowledge-editing\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eKnowledge Editing is a compelling field of research that focuses on facilitating efficient modifications to the behavior of models, particularly \u003cstrong\u003efoundation models\u003c/strong\u003e. The aim is to implement these changes within a specified scope of interest without negatively affecting the model's performance across a broader range of inputs.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eKeywords\u003c/h3\u003e\u003ca id=\"user-content-keywords\" class=\"anchor\" aria-label=\"Permalink: Keywords\" href=\"#keywords\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eKnowledge Editing has strong connections with following topics.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eUpdating and fixing bugs for large language models\u003c/li\u003e\n\u003cli\u003eLanguage models as knowledge base, locating knowledge in large language models\u003c/li\u003e\n\u003cli\u003eLifelong learning, unlearning and etc.\u003c/li\u003e\n\u003cli\u003eSecurity and privacy for large language models\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/zjunlp/KnowledgeEditingPapers/blob/main/img/ke.png\"\u003e\u003cimg src=\"/zjunlp/KnowledgeEditingPapers/raw/main/img/ke.png\" width=\"100%\" height=\"80%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eComparisons of different technologies\u003c/h2\u003e\u003ca id=\"user-content-comparisons-of-different-technologies\" class=\"anchor\" aria-label=\"Permalink: Comparisons of different technologies\" href=\"#comparisons-of-different-technologies\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/zjunlp/KnowledgeEditingPapers/blob/main/img/comparison.png\"\u003e\u003cimg src=\"/zjunlp/KnowledgeEditingPapers/raw/main/img/comparison.png\" width=\"60%\" height=\"48%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e📜 Resources\u003c/h2\u003e\u003ca id=\"user-content--resources\" class=\"anchor\" aria-label=\"Permalink: 📜 Resources\" href=\"#-resources\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis is a collection of research and review papers of Knowledge Editing. Any suggestions and pull requests are welcome for better sharing of latest research progress.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTutorials\u003c/h3\u003e\u003ca id=\"user-content-tutorials\" class=\"anchor\" aria-label=\"Permalink: Tutorials\" href=\"#tutorials\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eKnowledge Editing for Large Language Models, AAAI 2024 Tutorial\u003c/strong\u003e \u003cbr\u003e\nNingyu Zhang, Jia-Chen Gu, Yunzhi Yao, Zhen Bi, Shumin Deng. [\u003ca href=\"https://github.com/zjunlp/KnowledgeEditingPapers/blob/main/AAAI2024%40Tutorial_Knowledge%20Editing%20for%20LLMs.pdf\"\u003eGithub\u003c/a\u003e] [\u003ca href=\"https://drive.google.com/file/d/1fkTbVeRJSWmU7fBDeNf1OhHEkLSofQde/view?usp=sharing\" rel=\"nofollow\"\u003eGoogle Drive\u003c/a\u003e] [\u003ca href=\"https://pan.baidu.com/s/1oJYgaMnxWIBE4kIcJuMSKg?pwd=p9j5\" rel=\"nofollow\"\u003eBaidu Pan\u003c/a\u003e]\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEditing Large Language Models, AACL 2023 Tutorial\u003c/strong\u003e \u003cbr\u003e\nNingyu Zhang, Yunzhi Yao, Shumin Deng. [\u003ca href=\"https://github.com/zjunlp/KnowledgeEditingPapers/blob/main/AACL2023%40Tutorial_Editing%20LLMs.pdf\"\u003eGithub\u003c/a\u003e] [\u003ca href=\"https://drive.google.com/file/d/1EW-cusC_llCM0wEshkIdYuYrvfBPCDRz/view?usp=sharing\" rel=\"nofollow\"\u003eGoogle Drive\u003c/a\u003e] [\u003ca href=\"https://pan.baidu.com/s/1NupastGJUzcUIAjI64J1tw?pwd=i5an\" rel=\"nofollow\"\u003eBaidu Pan\u003c/a\u003e]\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSurveys\u003c/h3\u003e\u003ca id=\"user-content-surveys\" class=\"anchor\" aria-label=\"Permalink: Surveys\" href=\"#surveys\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eKnowledge Mechanisms in Large Language Models: A Survey and Perspective\u003c/strong\u003e (EMNLP 2024 Findings) \u003cbr\u003e\nMengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang. [\u003ca href=\"https://arxiv.org/abs/2407.15017\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Comprehensive Study of Knowledge Editing for Large Language Models\u003c/strong\u003e \u003cbr\u003e\nNingyu Zhang, Yunzhi Yao, Bozhong Tian, Peng Wang, Shumin Deng, Mengru Wang, Zekun Xi, Shengyu Mao, Jintian Zhang, Yuansheng Ni, Siyuan Cheng, Ziwen Xu, Xin Xu, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Lei Liang, Zhiqiang Zhang, Xiaowei Zhu, Jun Zhou, Huajun Chen.\n[\u003ca href=\"https://arxiv.org/abs/2401.01286\" rel=\"nofollow\"\u003epaper\u003c/a\u003e][\u003ca href=\"https://huggingface.co/datasets/zjunlp/KnowEdit\" rel=\"nofollow\"\u003ebenchmark\u003c/a\u003e][\u003ca href=\"https://github.com/zjunlp/EasyEdit\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEditing Large Language Models: Problems, Methods, and Opportunities, EMNLP 2023 Main Conference Paper\u003c/strong\u003e \u003cbr\u003e\nYunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang. [\u003ca href=\"https://arxiv.org/abs/2305.13172\" rel=\"nofollow\"\u003epaper\u003c/a\u003e][\u003ca href=\"https://github.com/zjunlp/EasyEdit\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eKnowledge Editing for Large Language Models: A Survey\u003c/strong\u003e \u003cbr\u003e\nSong Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, Jundong Li. [\u003ca href=\"https://arxiv.org/abs/2310.16218\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Survey on Knowledge Editing of Neural Networks\u003c/strong\u003e \u003cbr\u003e\nVittorio Mazzia, Alessandro Pedrani, Andrea Caciolai, Kay Rottmann, Davide Bernardi. [\u003ca href=\"https://arxiv.org/abs/2310.19704\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eKnowledge Unlearning for LLMs: Tasks, Methods, and Challenges\u003c/strong\u003e \u003cbr\u003e\nNianwen Si, Hao Zhang, Heyu Chang, Wenlin Zhang, Dan Qu, Weiqiang Zhang. [\u003ca href=\"https://arxiv.org/abs/2311.15766\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/zjunlp/KnowledgeEditingPapers/blob/main/img/overview.jpg\"\u003e\u003cimg src=\"/zjunlp/KnowledgeEditingPapers/raw/main/img/overview.jpg\" width=\"100%\" height=\"80%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMethods\u003c/h3\u003e\u003ca id=\"user-content-methods\" class=\"anchor\" aria-label=\"Permalink: Methods\" href=\"#methods\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePreserve Parameters\u003c/h4\u003e\u003ca id=\"user-content-preserve-parameters\" class=\"anchor\" aria-label=\"Permalink: Preserve Parameters\" href=\"#preserve-parameters\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMemory-based\u003c/h5\u003e\u003ca id=\"user-content-memory-based\" class=\"anchor\" aria-label=\"Permalink: Memory-based\" href=\"#memory-based\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMemory-Based Model Editing at Scale\u003c/strong\u003e (ICML 2022) \u003cbr\u003e\nEric Mitchell, Charles Lin, Antoine Bosselut, Christopher D. Manning, Chelsea Finn.\n[\u003ca href=\"https://arxiv.org/abs/2206.06520\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/eric-mitchell/serac\"\u003ecode\u003c/a\u003e] [\u003ca href=\"https://sites.google.com/view/serac-editing\" rel=\"nofollow\"\u003edemo\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFixing Model Bugs with Natural Language Patches\u003c/strong\u003e. (EMNLP 2022) \u003cbr\u003e\nShikhar Murty, Christopher D. Manning, Scott M. Lundberg, Marco Túlio Ribeiro.\n[\u003ca href=\"https://arxiv.org/abs/2211.03318\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/MurtyShikhar/LanguagePatching\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMemPrompt: Memory-assisted Prompt Editing with User Feedback\u003c/strong\u003e. (EMNLP 2022) \u003cbr\u003e\nAman Madaan, Niket Tandon, Peter Clark, Yiming Yang.\n[\u003ca href=\"https://arxiv.org/abs/2201.06009\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/madaan/memprompt\"\u003ecode\u003c/a\u003e] [\u003ca href=\"https://memprompt.com/\" rel=\"nofollow\"\u003epage\u003c/a\u003e] [\u003ca href=\"https://www.youtube.com/watch?v=Ld7R02bOiNQ\u0026amp;t=1s\" rel=\"nofollow\"\u003evideo\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLarge Language Models with Controllable Working Memory\u003c/strong\u003e. \u003cbr\u003e\nDaliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix Yu, Sanjiv Kumar.\n[\u003ca href=\"https://arxiv.org/abs/2211.05110\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCan We Edit Factual Knowledge by In-Context Learning?\u003c/strong\u003e \u003cbr\u003e\nCe Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, Baobao Chang.\n[\u003ca href=\"https://arxiv.org/abs/2305.12740\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCan LMs Learn New Entities from Descriptions? Challenges in Propagating Injected Knowledge\u003c/strong\u003e \u003cbr\u003e\nYasumasa Onoe, Michael J.Q. Zhang, Shankar Padmanabhan, Greg Durrett, Eunsol Choi.\n[\u003ca href=\"https://arxiv.org/abs/2305.01651\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMQUAKE: Assessing Knowledge Editing inLanguage Models via Multi-Hop Questions\u003c/strong\u003e \u003cbr\u003e\nZexuan Zhong, Zhengxuan Wu, Christopher D. Manning, Christopher Potts, Danqi Chen.\u003cbr\u003e[\u003ca href=\"https://arxiv.org/abs/2305.14795\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/princeton-nlp/MQuAKE\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePokeMQA: Programmable knowledge editing for Multi-hop Question Answering\u003c/strong\u003e \u003cbr\u003e\nHengrui Gu, Kaixiong Zhou, Xiaotian Han, Ninghao Liu, Ruobing Wang, Xin Wang. \u003cbr\u003e [\u003ca href=\"https://arxiv.org/abs/2312.15194\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/Hengrui-Gu/PokeMQA\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRetrieval-augmented Multilingual Knowledge Editing\u003c/strong\u003e \u003cbr\u003e\nWeixuan Wang, Barry Haddow, Alexandra Birch.\n[\u003ca href=\"https://arxiv.org/abs/2312.13040\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/Vicky-Wil/ReMaKE\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMEMORYLLM: Towards Self-Updatable Large Language Models\u003c/strong\u003e \u003cbr\u003e\nYu Wang, Xiusi Chen, Jingbo Shang, Julian McAuley. [\u003ca href=\"https://arxiv.org/abs/2402.04624\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDeepEdit: Knowledge Editing as Decoding with Constraints\u003c/strong\u003e \u003cbr\u003e\nYiwei Wang,Muhao Chen,Nanyun Peng, Kai-Wei Chang. [\u003ca href=\"https://arxiv.org/abs/2401.10471\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eStable Knowledge Editing in Large Language Models\u003c/strong\u003e. \u003cbr\u003e\nZihao Wei,Liang Pang,Hanxing Ding,Jingcheng Deng,Huawei Shen,Xueqi Cheng. [\u003ca href=\"https://arxiv.org/abs/2402.13048\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eKnowledge Editing on Black-box Large Language Models\u003c/strong\u003e. \u003cbr\u003e\nXiaoshuai Song, Zhengyang Wang, Keqing He, Guanting Dong, Jinxu Zhao, Weiran Xu. [\u003ca href=\"https://arxiv.org/abs/2402.08631\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLearning to Edit: Aligning LLMs with Knowledge Editing\u003c/strong\u003e. \u003cbr\u003e\nYuxin Jiang, Yufei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu, Wei Wang. [\u003ca href=\"https://arxiv.org/abs/2402.11905\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRobust and Scalable Model Editing for Large Language Models\u003c/strong\u003e. \u003cbr\u003e\nYingfa Chen, Zhengyan Zhang, Xu Han, Chaojun Xiao, Zhiyuan Liu, Chen Chen, Kuai Li, Tao Yang, Maosong Sun. [\u003ca href=\"https://arxiv.org/abs/2403.17431\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRetrieval-Enhanced Knowledge Editing for Multi-Hop Question Answering in Language Models\u003c/strong\u003e. \u003cbr\u003e\nYucheng Shi, Qiaoyu Tan, Xuansheng Wu, Shaochen Zhong, Kaixiong Zhou, Ninghao Liu. [\u003ca href=\"https://arxiv.org/abs/2403.19631\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eIn-Context Editing: Learning Knowledge from Self-Induced Distributions\u003c/strong\u003e. \u003cbr\u003e\nSiyuan Qi, Bangcheng Yang, Kailin Jiang, Xiaobo Wang, Jiaqi Li, Yifan Zhong, Yaodong Yang, Zilong Zheng. [\u003ca href=\"https://arxiv.org/abs/2406.11194\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCross-Lingual Multi-Hop Knowledge Editing\u003c/strong\u003e. \u003cbr\u003e\nAditi Khandelwal, Harman Singh, Hengrui Gu, Tianlong Chen, Kaixiong Zhou. [\u003ca href=\"https://arxiv.org/abs/2407.10275\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAnyEdit: Edit Any Knowledge Encoded in Language Models\u003c/strong\u003e. \u003cbr\u003e\nHoucheng Jiang, Junfeng Fang, Ningyu Zhang, Guojun Ma, Mingyang Wan, Xiang Wang, Xiangnan He, Tat-seng Chua. [\u003ca href=\"https://arxiv.org/abs/2502.05628\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAdditional Parameters\u003c/h5\u003e\u003ca id=\"user-content-additional-parameters\" class=\"anchor\" aria-label=\"Permalink: Additional Parameters\" href=\"#additional-parameters\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCalibrating Factual Knowledge in Pretrained Language Models\u003c/strong\u003e. (EMNLP 2022) \u003cbr\u003e\nQingxiu Dong, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, Lei Li.\n[\u003ca href=\"https://arxiv.org/abs/2210.03329\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/dqxiu/CaliNet\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTransformer-Patcher: One Mistake worth One Neuron\u003c/strong\u003e. (ICLR 2023) \u003cbr\u003e\nZeyu Huang, Yikang Shen, Xiaofeng Zhang, Jie Zhou, Wenge Rong, Zhang Xiong.\n[\u003ca href=\"https://arxiv.org/abs/2301.09785\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/ZeroYuHuang/Transformer-Patcher\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors\u003c/strong\u003e. (NeurIPS 2023) \u003cbr\u003e\nThomas Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, Marzyeh Ghassemi.\n[\u003ca href=\"https://arxiv.org/abs/2211.11031\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/thartvigsen/grace\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNeural Knowledge Bank for Pretrained Transformers\u003c/strong\u003e \u003cbr\u003e\nDamai Dai, Wenbin Jiang, Qingxiu Dong, Yajuan Lyu, Qiaoqiao She, Zhifang Sui. [\u003ca href=\"http://arxiv.org/abs/2208.00399\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRank-One Editing of Encoder-Decoder Models\u003c/strong\u003e \u003cbr\u003e\nVikas Raunak, Arul Menezes. [\u003ca href=\"https://arxiv.org/abs/2211.13317\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMELO: Enhancing Model Editing with Neuron-Indexed Dynamic LoRA.\u003c/strong\u003e (AAAI 2024) \u003cbr\u003e\nLang Yu, Qin Chen, Jie Zhou, Liang He. [\u003ca href=\"https://arxiv.org/abs/2312.11795\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/BruthYU/MELO\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMPN: Leveraging Multilingual Patch Neuron for Cross-lingual Model Editing\u003c/strong\u003e \u003cbr\u003e\nNianwen Si, Hao Zhang, Weiqiang Zhang. [\u003ca href=\"https://arxiv.org/abs/2401.03190\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSWEA: Changing Factual Knowledge in Large Language Models via Subject Word Embedding Altering\u003c/strong\u003e \u003cbr\u003e\nXiaopeng Li, Shasha Li, Bin Ji, Shezheng Song. [\u003ca href=\"https://arxiv.org/abs/2401.17809\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMEMoE: Enhancing Model Editing with Mixture of Experts Adaptors\u003c/strong\u003e \u003cbr\u003e\nRenzhi Wang, Piji Li. [\u003ca href=\"https://arxiv.org/abs/2405.19086\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eWISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models\u003c/strong\u003e. (NeurIPS 2024) \u003cbr\u003e\nPeng Wang, Zexi Li, Ningyu Zhang, Ziwen Xu, Yunzhi Yao, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen. [\u003ca href=\"https://arxiv.org/abs/2405.14768\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMEMLA: Enhancing Multilingual Knowledge Editing with Neuron-Masked Low-Rank Adaptation\u003c/strong\u003e. \u003cbr\u003e\nJiakuan Xie, Pengfei Cao, Yuheng Chen, Yubo Chen, Kang Liu, Jun Zhao. [\u003ca href=\"https://arxiv.org/abs/2405.14768\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eChange LM's representation space\u003c/h5\u003e\u003ca id=\"user-content-change-lms-representation-space\" class=\"anchor\" aria-label=\"Permalink: Change LM's representation space\" href=\"#change-lms-representation-space\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eInspecting and Editing Knowledge Representations in Language Models\u003c/strong\u003e \u003cbr\u003e\nEvan Hernandez, Belinda Z. Li, Jacob Andreas.\n[\u003ca href=\"http://arxiv.org/abs/2304.00740\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/evandez/REMEDI\"\u003ecode\u003c/a\u003e]\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModify Parameters\u003c/h4\u003e\u003ca id=\"user-content-modify-parameters\" class=\"anchor\" aria-label=\"Permalink: Modify Parameters\" href=\"#modify-parameters\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFinetuning\u003c/h5\u003e\u003ca id=\"user-content-finetuning\" class=\"anchor\" aria-label=\"Permalink: Finetuning\" href=\"#finetuning\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePlug-and-Play Adaptation for Continuously-updated QA\u003c/strong\u003e. (ACL 2022 Findings) \u003cbr\u003e\nKyungjae Lee, Wookje Han, Seung-won Hwang, Hwaran Lee, Joonsuk Park, Sang-Woo Lee.\n[\u003ca href=\"https://arxiv.org/abs/2204.12785\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/wookjeHan/Plug-and-Play-Adaptation-for-Continuously-updated-QA\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eModifying Memories in Transformer Models\u003c/strong\u003e. \u003cbr\u003e\nChen Zhu, Ankit Singh Rawat, Manzil Zaheer, Srinadh Bhojanapalli, Daliang Li, Felix Yu, Sanjiv Kumar.\n[\u003ca href=\"https://arxiv.org/abs/2012.00363\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eForgetting before Learning: Utilizing Parametric Arithmetic for Knowledge\nUpdating in Large Language Models\u003c/strong\u003e \u003cbr\u003e\nShiwen Ni, Dingwei Chen, Chengming Li, Xiping Hu, Ruifeng Xu and Min Yang. [\u003ca href=\"https://arxiv.org/pdf/2311.08011.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLLM Surgery: Efficient Knowledge Unlearning and Editing in Large Language Models\u003c/strong\u003e \u003cbr\u003e\nAkshaj Kumar Veldanda, Shi-Xiong Zhang, Anirban Das, Supriyo Chakraborty, Stephen Rawls, Sambit Sahu, Milind Naphade. [\u003ca href=\"https://arxiv.org/abs/2409.13054\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMeta-learning\u003c/h5\u003e\u003ca id=\"user-content-meta-learning\" class=\"anchor\" aria-label=\"Permalink: Meta-learning\" href=\"#meta-learning\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEditing Factual Knowledge in Language Models\u003c/strong\u003e. (EMNLP 2021) \u003cbr\u003e\nNicola De Cao, Wilker Aziz, Ivan Titov.\n[\u003ca href=\"https://arxiv.org/abs/2104.08164\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/nicola-decao/KnowledgeEditor\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFast Model Editing at Scale\u003c/strong\u003e. (ICLR 2022) \u003cbr\u003e\nEric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, Christopher D. Manning.\n[\u003ca href=\"https://arxiv.org/abs/2110.11309\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/eric-mitchell/mend\"\u003ecode\u003c/a\u003e] [\u003ca href=\"https://sites.google.com/view/mend-editing\" rel=\"nofollow\"\u003epage\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEditable Neural Networks\u003c/strong\u003e. (ICLR 2020) \u003cbr\u003e\nAnton Sinitsin, Vsevolod Plokhotnyuk, Dmitry V. Pyrkin, Sergei Popov, Artem Babenko. [\u003ca href=\"https://arxiv.org/abs/2004.00345\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/xtinkt/editable\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEditing Language Model-based Knowledge Graph Embeddings\u003c/strong\u003e (AAAI 2024) \u003cbr\u003e\nSiyuan Cheng, Ningyu Zhang, Bozhong Tian, Xi Chen, Qingbing Liu, Huajun Chen.\n[\u003ca href=\"https://arxiv.org/abs/2301.10405\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/zjunlp/PromptKG/tree/main/deltaKG\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMassive Editing for Large Language Model via Meta Learning.\u003c/strong\u003e (ICLR 2024) \u003cbr\u003e\nChenmien Tan1, Ge Zhang, Jie Fu. [\u003ca href=\"https://arxiv.org/pdf/2311.04661.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/ChenmienTan/malmen\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLocate and edit\u003c/h5\u003e\u003ca id=\"user-content-locate-and-edit\" class=\"anchor\" aria-label=\"Permalink: Locate and edit\" href=\"#locate-and-edit\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEditing a classifier by rewriting its prediction rules\u003c/strong\u003e. (NeurIPS 2021) \u003cbr\u003e\nShibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, Aleksander Madry.\n[\u003ca href=\"https://proceedings.neurips.cc/paper/2021/hash/c46489a2d5a9a9ecfc53b17610926ddd-Abstract.html\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/MadryLab/EditingClassifiers\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLanguage Anisotropic Cross-Lingual Model Editing\u003c/strong\u003e. \u003cbr\u003e\nYang Xu, Yutai Hou, Wanxiang Che.\n[\u003ca href=\"https://arxiv.org/abs/2205.12677\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRepairing Neural Networks by Leaving the Right Past Behind\u003c/strong\u003e. \u003cbr\u003e\nRyutaro Tanno, Melanie F. Pradier, Aditya Nori, Yingzhen Li.\n[\u003ca href=\"https://arxiv.org/abs/2207.04806\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLocating and Editing Factual Associations in GPT\u003c/strong\u003e. (NeurIPS 2022) \u003cbr\u003e\nKevin Meng, David Bau, Alex Andonian, Yonatan Belinkov.\n[\u003ca href=\"https://arxiv.org/abs/2202.05262\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/kmeng01/rome\"\u003ecode\u003c/a\u003e] [\u003ca href=\"https://rome.baulab.info/\" rel=\"nofollow\"\u003epage\u003c/a\u003e] [\u003ca href=\"https://www.youtube.com/watch?v=_NMQyOu2HTo\u0026amp;t=0\" rel=\"nofollow\"\u003evideo\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMass-Editing Memory in a Transformer\u003c/strong\u003e. (ICLR 2023) \u003cbr\u003e\nKevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, David Bau.\n[\u003ca href=\"https://arxiv.org/abs/2210.07229\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/kmeng01/memit\"\u003ecode\u003c/a\u003e] [\u003ca href=\"https://memit.baulab.info/\" rel=\"nofollow\"\u003epage\u003c/a\u003e] [\u003ca href=\"https://memit.baulab.us/#/\" rel=\"nofollow\"\u003edemo\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEditing models with task arithmetic\u003c/strong\u003e. (ICLR 2023) \u003cbr\u003e\nGabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, Ali Farhadi.\n[\u003ca href=\"https://openreview.net/pdf?id=6t0Kwf8-jrj\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEditing Common Sense in Transformers\u003c/strong\u003e. (EMNLP 2023) \u003cbr\u003e\nAnshita Gupta, Debanjan Mondal, Akshay Krishna Sheshadri, Wenlong Zhao, Xiang Lorraine Li, Sarah Wiegreffe, Niket Tandon.\n[\u003ca href=\"https://arxiv.org/abs/2305.14956\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDo Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs\u003c/strong\u003e. (EACL 2023) \u003cbr\u003e\nPeter Hase, Mona Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, Srinivasan Iyer.\n[\u003ca href=\"https://arxiv.org/pdf/2111.13654.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/peterbhase/SLAG-Belief-Updating\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDetecting Edit Failures In Large Language Models: An Improved Specificity Benchmark\u003c/strong\u003e. (ACL 2023 Findings)\u003cbr\u003e\nJason Hoelscher-Obermaier, Julia Persson, Esben Kran, Ioannis Konstas, Fazl Barez.\n[\u003ca href=\"https://arxiv.org/abs/2305.17553\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eKnowledge Neurons in Pretrained Transformers\u003c/strong\u003e.(ACL 2022) \u003cbr\u003e\nDamai Dai , Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, Furu Wei.[\u003ca href=\"http://arxiv.org/abs/2104.08696\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/Hunter-DDM/knowledge-neurons\"\u003ecode\u003c/a\u003e] [\u003ca href=\"https://github.com/EleutherAI/knowledge-neurons\"\u003ecode by EleutherAI\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLEACE: Perfect linear concept erasure in closed form\u003c/strong\u003e .\u003cbr\u003e\nNora Belrose, David Schneider-Joseph, Shauli Ravfogel, Ryan Cotterell, Edward Raff, Stella Biderman.\n[\u003ca href=\"https://arxiv.org/abs/2306.03819\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTransformer Feed-Forward Layers Are Key-Value Memories\u003c/strong\u003e. (EMNLP 2021) \u003cbr\u003e\nMor Geva, Roei Schuster, Jonathan Berant, Omer Levy. [\u003ca href=\"https://arxiv.org/abs/2012.14913\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTransformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space\u003c/strong\u003e.(EMNLP 2022) \u003cbr\u003e\nMor Geva, Avi Caciularu, Kevin Ro Wang, Yoav Goldberg. [\u003ca href=\"https://arxiv.org/abs/2203.14680\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePMET: Precise Model Editing in a Transformer.\u003c/strong\u003e (AAAI 2024) \u003cbr\u003e\nXiaopeng Li, Shasha Li, Shezheng Song, Jing Yang, Jun Ma, Jie Yu. [\u003ca href=\"https://arxiv.org/abs/2308.08742\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/xpq-tech/PMET.git\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eUnlearning Bias in Language Models by Partitioning Gradients.\u003c/strong\u003e (ACL 2023 Findings) \u003cbr\u003e\nCharles Yu, Sullam Jeoung, Anish Kasi, Pengfei Yu, Heng Ji. [\u003ca href=\"https://aclanthology.org/2023.findings-acl.375.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/CharlesYu2000/PCGU-UnlearningBias\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models\u003c/strong\u003e (EMNLP 2023) \u003cbr\u003e\nXinwei Wu, Junzhuo Li, Minghui Xu, Weilong Dong, Shuangzhi Wu, Chao Bian, Deyi Xiong. [\u003ca href=\"https://arxiv.org/pdf/2310.20138.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eUntying the Reversal Curse via Bidirectional Language Model Editing\u003c/strong\u003e \u003cbr\u003e\nJun-Yu Ma, Jia-Chen Gu, Zhen-Hua Ling, Quan Liu, Cong Liu. [\u003ca href=\"https://arxiv.org/pdf/2310.10322.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTrace and Edit Relation Associations in GPT\u003c/strong\u003e \u003cbr\u003e\nJiahang Li,Taoyu Chen,Yuanli Wang. [\u003ca href=\"https://arxiv.org/abs/2401.02976\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eConsecutive Model Editing with Batch alongside HooK Layers\u003c/strong\u003e \u003cbr\u003e\nShuaiyi Li,Yang Deng,Deng Cai,Hongyuan Lu,Liang Chen,Wai Lam. [\u003ca href=\"https://arxiv.org/abs/2403.05330\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA Unified Framework for Model Editing\u003c/strong\u003e \u003cbr\u003e\nAkshat Gupta,Dev Sajnani,Gopala Anumanchipalli. [\u003ca href=\"https://arxiv.org/abs/2403.14236\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDetoxifying Large Language Models via Knowledge Editing\u003c/strong\u003e \u003cbr\u003e\nMengru Wang, Ningyu Zhang, Ziwen Xu, Zekun Xi, Shumin Deng, Yunzhi Yao, Qishen Zhang, Linyi Yang, Jindong Wang, Huajun Chen. [\u003ca href=\"https://arxiv.org/abs/2403.14472\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLocating and Editing Factual Associations in Mamba\u003c/strong\u003e \u003cbr\u003e\nArnab Sen Sharma,David Atkinson,David Bau. [\u003ca href=\"https://arxiv.org/abs/2404.03646\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLarge Language Model Bias Mitigation from the Perspective of Knowledge Editing\u003c/strong\u003e \u003cbr\u003e\nRuizhe Chen, Yichen Li, Zikai Xiao, Zuozhu Liu. [\u003ca href=\"https://arxiv.org/abs/2405.09341\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eWilKE: Wise-Layer Knowledge Editor for Lifelong Knowledge Editing\u003c/strong\u003e \u003cbr\u003e\nChenhui Hu, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao. [\u003ca href=\"https://arxiv.org/abs/2402.10987\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eReFACT: Updating Text-to-Image Models by Editing the Text Encoder\u003c/strong\u003e \u003cbr\u003e\nDana Arad, Hadas Orgad, Yonatan Belinkov. [\u003ca href=\"https://arxiv.org/pdf/2303.08084\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEditing Implicit Assumptions in Text-to-Image Diffusion Models\u003c/strong\u003e \u003cbr\u003e\nHadas Orgad, Bahjat Kawar, Yonatan Belinkov. [\u003ca href=\"https://arxiv.org/pdf/2303.08084\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMass-Editing Memory with Attention in Transformers: A cross-lingual exploration of knowledge\u003c/strong\u003e \u003cbr\u003e\nDaniel Tamayo, Aitor Gonzalez-Agirre, Javier Hernando, Marta Villegas. [\u003ca href=\"https://arxiv.org/abs/2502.02173\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMore Related Papers\u003c/h4\u003e\u003ca id=\"user-content-more-related-papers\" class=\"anchor\" aria-label=\"Permalink: More Related Papers\" href=\"#more-related-papers\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFRUIT: Faithfully Reflecting Updated Information in Text\u003c/strong\u003e. (NAACL 2022) \u003cbr\u003e\nRobert L. Logan IV, Alexandre Passos, Sameer Singh, Ming-Wei Chang.\n[\u003ca href=\"https://arxiv.org/pdf/2112.08634\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/google-research/language/tree/master/language/fruit\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEntailer: Answering Questions with Faithful and Truthful Chains of Reasoning\u003c/strong\u003e. (EMNLP 2022) \u003cbr\u003e\nOyvind Tafjord, Bhavana Dalvi Mishra, Peter Clark.\n[\u003ca href=\"https://arxiv.org/abs/2210.12217\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/allenai/entailment_bank\"\u003ecode\u003c/a\u003e] [\u003ca href=\"https://www.youtube.com/watch?v=GYTJ_Pxva7Q\" rel=\"nofollow\"\u003evideo\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTowards Tracing Factual Knowledge in Language Models Back to the Training Data\u003c/strong\u003e. \u003cbr\u003e\nEkin Akyürek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong, Ian Tenney, Jacob Andreas, Kelvin Guu.\n(EMNLP 2022) [\u003ca href=\"https://arxiv.org/abs/2204.12785\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePrompting GPT-3 To Be Reliable\u003c/strong\u003e. \u003cbr\u003e\nChenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan Boyd-Graber, Lijuan Wang.\n[\u003ca href=\"https://arxiv.org/abs/2210.09150\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePatching open-vocabulary models by interpolating weights\u003c/strong\u003e. (NeurIPS 2022) \u003cbr\u003e\nGabriel Ilharco, Mitchell Wortsman, Samir Yitzhak Gadre, Shuran Song, Hannaneh Hajishirzi, Simon Kornblith, Ali Farhadi, Ludwig Schmidt.\n[\u003ca href=\"https://arxiv.org/abs/2208.05592\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/mlfoundations/patching\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDecouple knowledge from paramters for plug-and-play language modeling\u003c/strong\u003e (ACL2023 Findings) \u003cbr\u003e\nXin Cheng, Yankai Lin, Xiuying Chen, Dongyan Zhao, Rui Yan.[\u003ca href=\"http://arxiv.org/abs/2305.11564\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/Hannibal046/PlugLM\"\u003ecode\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBackpack Language Models\u003c/strong\u003e \u003cbr\u003e\nJohn Hewitt, John Thickstun, Christopher D. Manning, Percy Liang. [\u003ca href=\"https://arxiv.org/pdf/2305.16765.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLearning to Model Editing Processes\u003c/strong\u003e. (EMNLP 2022) \u003cbr\u003e\nMachel Reid, Graham Neubig. [\u003ca href=\"https://aclanthology.org/2022.findings-emnlp.280.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTrends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications\u003c/strong\u003e. \u003cbr\u003e\nZhangyin Feng, Weitao Ma, Weijiang Yu, Lei Huang, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, Ting liu. [\u003ca href=\"https://arxiv.org/pdf/2311.05876.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDUnE: Dataset for Unified Editing\u003c/strong\u003e. (EMNLP 2023) \u003cbr\u003e\nAfra Feyza Akyürek, Eric Pan, Garry Kuwanto, Derry Wijaya. [\u003ca href=\"https://arxiv.org/abs/2311.16087\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSee the Unseen: Better Context-Consistent Knowledge-Editing by Noises\u003c/strong\u003e. \u003cbr\u003e\nYoucheng Huang, Wenqiang Lei, Zheng Zhang, Jiancheng Lv, Shuicheng Yan. [\u003ca href=\"https://arxiv.org/abs/2401.07544\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSowing the Wind, Reaping the Whirlwind: The Impact of Editing Language Models\u003c/strong\u003e. \u003cbr\u003e\nRima Hazra, Sayan Layek, Somnath Banerjee, Soujanya Poria. [\u003ca href=\"https://arxiv.org/pdf/2401.10647.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eModel Editing with Canonical Examples\u003c/strong\u003e. \u003cbr\u003e\nJohn Hewitt, Sarah Chen, Lanruo Lora Xie. [\u003ca href=\"https://arxiv.org/pdf/2402.06155.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEVEDIT: Event-based Knowledge Editing with Deductive Editing Boundaries\u003c/strong\u003e. \u003cbr\u003e\nJiateng Liu,Pengfei Yu,Yuji Zhang,Sha Li,Zixuan Zhang,Heng Ji. [\u003ca href=\"https://arxiv.org/abs/2402.11324\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eInvestigating Multi-Hop Factual Shortcuts in Knowledge Editing of Large Language Models\u003c/strong\u003e. \u003cbr\u003e\nTianjie Ju,Yijin Chen,Xinwei Yuan,Zhuosheng Zhang,Wei Du,Yubin Zheng,Gongshen Liu. [\u003ca href=\"https://arxiv.org/abs/2402.11900\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eKnowledge Graph Enhanced Large Language Model Editing\u003c/strong\u003e. \u003cbr\u003e\nMengqi Zhang,Xiaotian Ye,Qiang Liu,Pengjie Ren,Shu Wu,Zhumin Chen. [\u003ca href=\"https://arxiv.org/abs/2402.13593\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEditing Factual Knowledge and Explanatory Ability of Medical Large Language Models\u003c/strong\u003e. \u003cbr\u003e\nDerong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin. [\u003ca href=\"https://arxiv.org/abs/2402.18099\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eKEBench: A Benchmark on Knowledge Editing for Large Vision-Language Models\u003c/strong\u003e. \u003cbr\u003e\nHan Huang, Haitian Zhong, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. [\u003ca href=\"https://arxiv.org/pdf/2403.07350.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCOLLABEDIT: TOWARDS NON-DESTRUCTIVE COLLABORATIVE KNOWLEDGE EDITING\u003c/strong\u003e. \u003cbr\u003e\nJiamu Zheng, Jinghuai Zhang, Futing Wang, Tianyu Du, Tao Lin. [\u003ca href=\"https://openreview.net/pdf?id=PoVpff2Udw\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTAXI: Evaluating Categorical Knowledge Editing for Language Models\u003c/strong\u003e. \u003cbr\u003e\nDerek Powell, Walter Gerych, Thomas Hartvigsen. [\u003ca href=\"https://arxiv.org/pdf/2404.15004\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLarge Scale Knowledge Washing\u003c/strong\u003e. \u003cbr\u003e\nYu Wang, Ruihan Wu, Zexue He, Xiusi Chen, Julian McAuley. [\u003ca href=\"https://arxiv.org/abs/2405.16720\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDefending Large Language Models Against Jailbreak Attacks via Layer-specific Editing\u003c/strong\u003e. \u003cbr\u003e\nWei Zhao, Zhe Li, Yige Li, Ye Zhang, Jun Sun. [\u003ca href=\"https://arxiv.org/abs/2406.02882\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOutdated Issue Aware Decoding for Factual Knowledge Editing\u003c/strong\u003e. \u003cbr\u003e\nZengkui Sun, Yijin Liu, Jiaan Wang, Fandong Meng, Jinan Xu, Yufeng Chen, Jie Zhou. [\u003ca href=\"https://arxiv.org/abs/2405.18166\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAdaptive Token Biaser: Knowledge Editing via Biasing Key Entities\u003c/strong\u003e. \u003cbr\u003e\nBaolong Bi, Shenghua Liu, Yiwei Wang, Lingrui Mei, Hongcheng Gao, Yilong Xu, Xueqi Cheng. [\u003ca href=\"https://arxiv.org/abs/2406.12468\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLanguage Modeling with Editable External Knowledge\u003c/strong\u003e. \u003cbr\u003e\nBelinda Z. Li, Emmy Liu, Alexis Ross, Abbas Zeitoun, Graham Neubig, Jacob Andreas. [\u003ca href=\"https://arxiv.org/abs/2406.11830\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSafety Arithmetic: A Framework for Test-time Safety Alignment of Language Models by Steering Parameters and Activations\u003c/strong\u003e. \u003cbr\u003e\nRima Hazra, Sayan Layek, Somnath Banerjee, Soujanya Poria. [\u003ca href=\"https://arxiv.org/abs/2406.11801\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEnhancing Data Privacy in Large Language Models through Private Association Editing\u003c/strong\u003e. \u003cbr\u003e\nKunquan Deng, Zeyu Huang, Chen Li, Chenghua Lin, Min Gao, Wenge Rong. [\u003ca href=\"https://arxiv.org/abs/2407.00488\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePFME: A Modular Approach for Fine-grained Hallucination Detection and Editing of Large Language Models\u003c/strong\u003e. \u003cbr\u003e\nDavide Venditti, Elena Sofia Ruzzetti, Giancarlo A. Xompero, Cristina Giannone, Andrea Favalli, Raniero Romagnoli, Fabio Massimo Zanzotto. [\u003ca href=\"https://arxiv.org/abs/2406.18221\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSafeInfer: Context Adaptive Decoding Time Safety Alignment for Large Language Models\u003c/strong\u003e. \u003cbr\u003e\nSomnath Banerjee, Soham Tripathy, Sayan Layek, Shanu Kumar, Animesh Mukherjee, Rima Hazra. [\u003ca href=\"https://arxiv.org/abs/2406.12274\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEditing Implicit Assumptions in Text-to-Image Diffusion Models\u003c/strong\u003e. (ICCV 2023) \u003cbr\u003e\nHadas Orgad, Bahjat Kawar, Yonatan Belinkov. [\u003ca href=\"https://arxiv.org/abs/2303.08084\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eReFACT: Updating Text-to-Image Models by Editing the Text Encoder\u003c/strong\u003e. (NAAC 2024) \u003cbr\u003e\nDana Arad, Hadas Orgad, Yonatan Belinkov. [\u003ca href=\"https://arxiv.org/abs/2306.00738\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMC-MKE: A Fine-Grained Multimodal Knowledge Editing Benchmark Emphasizing Modality Consistency\u003c/strong\u003e. \u003cbr\u003e\nJunzhe Zhang, Huixuan Zhang, Xunjian Yin, Baizhou Huang, Xu Zhang, Xinyu Hu, Xiaojun Wan. [\u003ca href=\"https://arxiv.org/abs/2406.13219\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLLM-Based Multi-Hop Question Answering with Knowledge Graph Integration in Evolving Environments\u003c/strong\u003e. \u003cbr\u003e\nRuirui Chen, Weifeng Jiang, Chengwei Qin, Ishaan Singh Rawal, Cheston Tan, Dongkyu Choi, Bo Xiong, Bo Ai. [\u003ca href=\"https://arxiv.org/abs/2408.15903\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePioneering Reliable Assessment in Text-to-Image Knowledge Editing: Leveraging a Fine-Grained Dataset and an Innovative Criterion\u003c/strong\u003e. \u003cbr\u003e\nHengrui Gu, Kaixiong Zhou, Yili Wang, Ruobing Wang, Xin Wang. [\u003ca href=\"https://arxiv.org/abs/2409.17928\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTowards Unified Multimodal Editing with Enhanced Knowledge Collaboration\u003c/strong\u003e. \u003cbr\u003e\nKaihang Pan, Zhaoyu Fan, Juncheng Li, Qifan Yu, Hao Fei, Siliang Tang, Richang Hong, Hanwang Zhang, Qianru Sun. [\u003ca href=\"https://arxiv.org/abs/2409.19872\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eKnowledge Editing with Dynamic Knowledge Graphs for Multi-hop Question Answering\u003c/strong\u003e. \u003cbr\u003e\nYifan Lu, Yigeng Zhou, Jing Li, Yequan Wang, Xuebo Liu, Daojing He, Fangming Liu, Min Zhang. [\u003ca href=\"https://arxiv.org/abs/2412.13782\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eEverything is Editable: Extend Knowledge Editing to Unstructured Data in Large Language Models\u003c/strong\u003e. \u003cbr\u003e\nJingcheng Deng, Zihao Wei, Liang Pang, Hanxing Ding, Huawei Shen, Xueqi Cheng. [\u003ca href=\"https://arxiv.org/abs/2405.15349\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSAKE: Steering Activations for Knowledge Editing\u003c/strong\u003e. \u003cbr\u003e\nMarco Scialanga, Thibault Laugel, Vincent Grari, Marcin Detyniecki. [\u003ca href=\"https://arxiv.org/abs/2503.01751\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAnalysis\u003c/h3\u003e\u003ca id=\"user-content-analysis\" class=\"anchor\" aria-label=\"Permalink: Analysis\" href=\"#analysis\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eDoes Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models.\u003c/strong\u003e \u003cbr\u003e\nPeter Hase, Mohit Bansal, Been Kim, Asma Ghandeharioun. [\u003ca href=\"https://arxiv.org/pdf/2301.04213.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/google/belief-localization\"\u003ecode\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDissecting Recall of Factual Associations in Auto-Regressive Language Models\u003c/strong\u003e \u003cbr\u003e\nMor Geva, Jasmijn Bastings, Katja Filippova, Amir Globerson. [\u003ca href=\"https://arxiv.org/abs/2304.14767\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eEvaluating the Ripple Effects of Knowledge Editing in Language Models\u003c/strong\u003e \u003cbr\u003e\nRoi Cohen, Eden Biran, Ori Yoran, Amir Globerson, Mor Geva. [\u003ca href=\"https://arxiv.org/abs/2307.12976\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eEdit at your own risk: evaluating the robustness of edited models to distribution shifts.\u003c/strong\u003e \u003cbr\u003e\nDavis Brown, Charles Godfrey, Cody Nizinski, Jonathan Tu, Henry Kvinge. [\u003ca href=\"https://arxiv.org/abs/2303.00046\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eJourney to the Center of the Knowledge Neurons: Discoveries of Language-Independent Knowledge Neurons and Degenerate Knowledge Neurons.\u003c/strong\u003e (AAAI 2024) \u003cbr\u003e\nYuheng Chen, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao. [\u003ca href=\"https://arxiv.org/abs/2308.13198\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLinearity of Relation Decoding in Transformer Language Models\u003c/strong\u003e\u003cbr\u003e\nEvan Hernandez, Martin Wattenberg, Arnab Sen Sharma, Jacob Andreas, Tal Haklay, Yonatan Belinkov, Kevin Meng, David Bau. [\u003ca href=\"https://arxiv.org/pdf/2308.09124.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKLoB: a Benchmark for Assessing Knowledge Locating Methods in Language Models\u003c/strong\u003e\u003cbr\u003e\nYiming Ju, Zheng Zhang. [\u003ca href=\"http://export.arxiv.org/pdf/2309.16535\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eInference-Time Intervention: Eliciting Truthful Answers from a Language Model\u003c/strong\u003e (NeurIPS 2023) \u003cbr\u003e\nKenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, Martin Wattenberg. [\u003ca href=\"https://arxiv.org/abs/2306.03341\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/likenneth/honest_llama\"\u003ecode\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eEmptying the Ocean with a Spoon: Should We Edit Models?\u003c/strong\u003e (EMNLP 2023 Findings) \u003cbr\u003e\nYuval Pinter and Michael Elhadad. [\u003ca href=\"https://arxiv.org/pdf/2310.11958.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eUnveiling the Pitfalls of Knowledge Editing for Large Language Models\u003c/strong\u003e \u003cbr\u003e\nZhoubo Li, Ningyu Zhang, Yunzhi Yao, Mengru Wang, Xi Chen and Huajun Chen. [\u003ca href=\"https://arxiv.org/pdf/2310.02129.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eEditing Personality for LLMs\u003c/strong\u003e \u003cbr\u003e\nShengyu Mao, Ningyu Zhang, Xiaohan Wang, Mengru Wang, Yunzhi Yao, Yong Jiang, Pengjun Xie, Fei Huang and Huajun Chen. [\u003ca href=\"https://arxiv.org/pdf/2310.02168.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eEvaluating Dependencies in Fact Editing for Language Models: Specificity and Implication Awareness\u003c/strong\u003e（Findings of EMNLP2023） \u003cbr\u003e\nZichao Li, Ines Arous, Siva Reddy, Jackie C.K. Cheung [\u003ca href=\"https://arxiv.org/pdf/2312.01858.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eFinding and Editing Multi-Modal Neurons in Pre-Trained Transformer\u003c/strong\u003e \u003cbr\u003e\nHaowen Pan,Yixin Cao,Xiaozhi Wang,Xun Yang. [\u003ca href=\"https://arxiv.org/abs/2311.07470\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eAssessing Knowledge Editing in Language Models via Relation Perspective\u003c/strong\u003e \u003cbr\u003e\nYifan Wei,Xiaoyan Yu,Huanhuan Ma,Fangyu Lei,Yixuan Weng,Ran Song,Kang Liu. [\u003ca href=\"https://arxiv.org/abs/2311.09053\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHistory Matters: Temporal Knowledge Editing in Large Language Model\u003c/strong\u003e（AAAI 2024） \u003cbr\u003e\nXunjian Yin,Jin Jiang,Liming Yang,Xiaojun Wan. [\u003ca href=\"https://arxiv.org/abs/2312.05497\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCross-Lingual Knowledge Editing in Large Language Models\u003c/strong\u003e \u003cbr\u003e\nJiaan Wang, Yunlong Liang, Zengkui Sun, Yuxuan Cao, Jiarong Xu. [\u003ca href=\"https://arxiv.org/pdf/2309.08952.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLarge Language Models Relearn Removed Concepts\u003c/strong\u003e \u003cbr\u003e\nMichelle Lo, Shay B. Cohen, Fazl Barez [\u003ca href=\"https://arxiv.org/abs/2401.01814\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eModel Editing Can Hurt General Abilities of Large Language Models\u003c/strong\u003e \u003cbr\u003e\nJia-Chen Gu, Hao-Xiang Xu, Jun-Yu Ma, Pan Lu, Zhen-Hua Ling, Kai-Wei Chang, Nanyun Peng [\u003ca href=\"https://arxiv.org/pdf/2401.04700.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eModel Editing at Scale leads to Gradual and Catastrophic Forgetting\u003c/strong\u003e \u003cbr\u003e\nAkshat Gupta, Anurag Rao, Gopala Anumanchipalli. [\u003ca href=\"https://arxiv.org/abs/2401.07453\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePropagation and Pitfalls: Reasoning-based Assessment of Knowledge Editing through Counterfactual Tasks\u003c/strong\u003e \u003cbr\u003e\nWenyue Hua, Jiang Guo, Mingwen Dong, Henghui Zhu, Patrick Ng, Zhiguo Wang. [\u003ca href=\"https://arxiv.org/abs/2401.17585\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLong-form evaluation of model editing\u003c/strong\u003e \u003cbr\u003e\nDomenic Rosati, Robie Gonzales, Jinkun Chen, Xuemin Yu. [\u003ca href=\"https://arxiv.org/pdf/2402.09394.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eThe Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse\u003c/strong\u003e \u003cbr\u003e\nWanli Yang, Fei Sun, Xinyu Ma, Xun Liu, Dawei Yin, Xueqi Cheng. [\u003ca href=\"https://arxiv.org/pdf/2402.09656.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eThe Da Vinci Code of Large Pre-trained Language Models: Deciphering Degenerate Knowledge Neurons\u003c/strong\u003e \u003cbr\u003e\nYuheng Chen,Pengfei Cao,Yubo Chen,Yining Wang,Shengping Liu,Kang Liu,Jun Zhao. [\u003ca href=\"https://arxiv.org/abs/2402.13731\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eNavigating the Dual Facets: A Comprehensive Evaluation of Sequential Memory Editing in Large Language Models\u003c/strong\u003e \u003cbr\u003e\nZihao Lin, Mohammad Beigi, Hongxuan Li, Yufan Zhou, Yuxiang Zhang, Qifan Wang, Wenpeng Yin, Lifu Huang. [\u003ca href=\"https://www.arxiv.org/abs/2402.11122\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e“Flex Tape Can’t Fix That”:Bias and Misinformation in Edited Language Models\u003c/strong\u003e \u003cbr\u003e\nKarina Halevy, Anna Sotnikova, Badr AlKhamissi, Syrielle Montariol, Antoine Bosselut. [\u003ca href=\"https://arxiv.org/pdf/2403.00180\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eThe Missing Piece in Model Editing: A Deep Dive into the Hidden Damage Brought By Model Editing\u003c/strong\u003e \u003cbr\u003e\nJianchen Wang,Zhouhong Gu,Zhuozhi Xiong,Hongwei Feng,Yanghua Xiao. [\u003ca href=\"https://arxiv.org/abs/2403.07825\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBeyond Memorization: The Challenge of Random Memory Access in Language Models\u003c/strong\u003e \u003cbr\u003e\nTongyao Zhu,Qian Liu,Liang Pang,Zhengbao Jiang,Min-Yen Kan,Min Lin. [\u003ca href=\"https://arxiv.org/abs/2403.07805\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eInterpreting Key Mechanisms of Factual Recall in Transformer-Based Language Models\u003c/strong\u003e \u003cbr\u003e\nAng Lv, Kaiyi Zhang, Yuhan Chen, Yulong Wang, Lifeng Liu, Ji-Rong Wen, Jian Xie, Rui Yan. [\u003ca href=\"https://arxiv.org/abs/2403.19521\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models\u003c/strong\u003e \u003cbr\u003e\nZihao Wei,Jingcheng Deng,Liang Pang,Hanxing Ding,Huawei Shen,Xueqi Cheng. [\u003ca href=\"https://arxiv.org/abs/2404.04990\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eIs Your LLM Outdated? Benchmarking LLMs \u0026amp; Alignment Algorithms for Time-Sensitive Knowledge\u003c/strong\u003e \u003cbr\u003e\nSeyed Mahed Mousavi, Simone Alghisi, Giuseppe Riccardi. [\u003ca href=\"https://arxiv.org/abs/2404.08700\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eNeighboring Perturbations of Knowledge Editing on Large Language Models\u003c/strong\u003e（ICML 2024）\u003cbr\u003e\nJun-Yu Ma, Jia-Chen Gu, Ningyu Zhang, Zhen-Hua Ling. [\u003ca href=\"https://arxiv.org/pdf/2401.17623\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eEvent-level Knowledge Editing\u003c/strong\u003e\u003cbr\u003e\nHao Peng, Xiaozhi Wang, Chunyang Li, Kaisheng Zeng, Jiangshan Duo, Yixin Cao, Lei Hou, Juanzi Li. [\u003ca href=\"https://arxiv.org/abs/2402.13093\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eUpdating Language Models with Unstructured Facts: Towards Practical Knowledge Editing\u003c/strong\u003e\u003cbr\u003e\nXiaobao Wu, Liangming Pan, William Yang Wang, Anh Tuan Luu. [\u003ca href=\"https://arxiv.org/abs/2402.18909\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDetecting Edited Knowledge in Language Models\u003c/strong\u003e\u003cbr\u003e\nPaul Youssef, Zhixue Zhao, Jörg Schlötterer, Christin Seifert. [\u003ca href=\"https://arxiv.org/abs/2405.02765\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePerturbation-Restrained Sequential Model Editing\u003c/strong\u003e\u003cbr\u003e\nJun-Yu Ma, Hong Wang, Hao-Xiang Xu, Zhen-Hua Ling, Jia-Chen Gu. [\u003ca href=\"https://arxiv.org/abs/2405.16821\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLeveraging Logical Rules in Knowledge Editing: A Cherry on the Top\u003c/strong\u003e\u003cbr\u003e\nKeyuan Cheng, Muhammad Asif Ali, Shu Yang, Gang Lin, Yuxuan Zhai, Haoyang Fei, Ke Xu, Lu Yu, Lijie Hu, Di Wang. [\u003ca href=\"https://arxiv.org/abs/2405.15452\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eModel Editing by Standard Fine-Tuning\u003c/strong\u003e\u003cbr\u003e\nGovind Gangadhar, Karl Stratos. [\u003ca href=\"https://arxiv.org/abs/2402.11078v3\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eAI-native Memory: A Pathway from LLMs Towards AGI\u003c/strong\u003e\u003cbr\u003e\nJingbo Shang, Zai Zheng, Xiang Ying, Felix Tao, Mindverse Team. [\u003ca href=\"https://arxiv.org/abs/2406.18312\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eIntrinsic Evaluation of Unlearning Using Parametric Knowledge Traces\u003c/strong\u003e\u003cbr\u003e\nYihuai Hong, Lei Yu, Shauli Ravfogel, Haiqin Yang, Mor Geva. [\u003ca href=\"https://arxiv.org/abs/2406.11614\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCan Editing LLMs Inject Harm?\u003c/strong\u003e\u003cbr\u003e\nCanyu Chen, Baixiang Huang, Zekun Li, Zhaorun Chen, Shiyang Lai, Xiongxiao Xu, Jia-Chen Gu, Jindong Gu, Huaxiu Yao, Chaowei Xiao, Xifeng Yan, William Yang Wang, Philip Torr, Dawn Song, Kai Shu. [\u003ca href=\"https://arxiv.org/abs/2407.20224\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eEditing Conceptual Knowledge for Large Language Models\u003c/strong\u003e (EMNLP 2024 Findings) \u003cbr\u003e\nXiaohan Wang, Shengyu Mao, Ningyu Zhang, Shumin Deng, Yunzhi Yao, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen. [\u003ca href=\"https://arxiv.org/abs/2403.06259\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKnowledge Circuits in Pretrained Transformers\u003c/strong\u003e (NeurIPS 2024) \u003cbr\u003e\nYunzhi Yao, Ningyu Zhang, Zekun Xi, Mengru Wang, Ziwen Xu, Shumin Deng, Huajun Chen. [\u003ca href=\"https://arxiv.org/abs/2405.17969\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\"Why\" Has the Least Side Effect on Model Editing\u003c/strong\u003e \u003cbr\u003e\nTsung-Hsuan Pan, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen. [\u003ca href=\"https://arxiv.org/abs/2409.18679\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCross-Lingual Multi-Hop Knowledge Editing\u003c/strong\u003e. \u003cbr\u003e\nAditi Khandelwal, Harman Singh, Hengrui Gu, Tianlong Chen, Kaixiong Zhou. [\u003ca href=\"https://arxiv.org/abs/2407.10275\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCan We Reverse In-Context Knowledge Edits?\u003c/strong\u003e\u003cbr\u003e\nPaul Youssef, Zhixue Zhao, Jörg Schlötterer, Christin Seifert. [\u003ca href=\"https://arxiv.org/abs/2410.12586\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eModel Editing for LLMs4Code: How Far are We?\u003c/strong\u003e\u003cbr\u003e\nXiaopeng Li, Shangwen Wang, Shasha Li, Jun Ma, Jie Yu, Xiaodong Liu, Jing Wang, Bin Ji, Weimin Zhang. [\u003ca href=\"https://arxiv.org/abs/2411.06638\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBackward Lens: Projecting Language Model Gradients into the Vocabulary Space\u003c/strong\u003e\u003cbr\u003e\nShahar Katz, Yonatan Belinkov, Mor Geva, Lior Wolf. [\u003ca href=\"https://arxiv.org/abs/2402.12865\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePosition: Editing Large Language Models Poses Serious Safety Risks\u003c/strong\u003e\u003cbr\u003e\nPaul Youssef, Zhixue Zhao, Daniel Braun, Jörg Schlötterer, Christin Seifert. [\u003ca href=\"https://arxiv.org/abs/2502.02958\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eThe Mirage of Model Editing: Revisiting Evaluation in the Wild\u003c/strong\u003e\u003cbr\u003e\nWanli Yang, Fei Sun, Jiajun Tan, Xinyu Ma, Qi Cao, Dawei Yin, Huawei Shen, Xueqi Cheng. [\u003ca href=\"https://arxiv.org/abs/2502.11177\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePosition: Editing Large Language Models Poses Serious Safety Risks\u003c/strong\u003e\u003cbr\u003e\nPaul Youssef, Zhixue Zhao, Daniel Braun, Jörg Schlötterer, Christin Seifert. [\u003ca href=\"https://arxiv.org/abs/2502.02958\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eConstraining Sequential Model Editing with Editing Anchor Compression\u003c/strong\u003e. \u003cbr\u003e\nHao-Xiang Xu, Jun-Yu Ma, Zhen-Hua Ling, Ningyu Zhang, Jia-Chen Gu. [\u003ca href=\"https://arxiv.org/abs/2503.00035\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🧰 Resources\u003c/h2\u003e\u003ca id=\"user-content--resources-1\" class=\"anchor\" aria-label=\"Permalink: 🧰 Resources\" href=\"#-resources-1\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBenchmarks and Tasks\u003c/h3\u003e\u003ca id=\"user-content-benchmarks-and-tasks\" class=\"anchor\" aria-label=\"Permalink: Benchmarks and Tasks\" href=\"#benchmarks-and-tasks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eEdit Type\u003c/th\u003e\n\u003cth\u003eBenchmarks \u0026amp; Datasets\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003eFact Knowledge\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/nicola-decao/KnowledgeEditor\"\u003eZSRE\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/2305.13172\" rel=\"nofollow\"\u003eZSRE plus\u003c/a\u003e, \u003ca href=\"https://rome.baulab.info/\" rel=\"nofollow\"\u003eCounterFact\u003c/a\u003e,\u003ca href=\"https://arxiv.org/abs/2305.13172\" rel=\"nofollow\"\u003eCounterFact plus\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/2305.17553\" rel=\"nofollow\"\u003eCounterFact+\u003c/a\u003e,\u003ca href=\"https://github.com/yasumasaonoe/entity_knowledge_propagation\"\u003eECBD\u003c/a\u003e, \u003ca href=\"https://github.com/yasumasaonoe/entity_knowledge_propagation\"\u003eMQUAKE\u003c/a\u003e,\u003ca href=\"https://arxiv.org/abs/2312.01858\" rel=\"nofollow\"\u003eDepEdit\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003eMulti-Lingual\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/pdf/2309.08952.pdf\" rel=\"nofollow\"\u003eBi-ZsRE\u003c/a\u003e,\u003ca href=\"https://arxiv.org/pdf/2308.09954.pdf\" rel=\"nofollow\"\u003eEva-KELLM\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/2312.13040\" rel=\"nofollow\"\u003eMzsRE\u003c/a\u003e,\u003ca href=\"https://www.arxiv.org/abs/2407.10275\" rel=\"nofollow\"\u003eCROLIN-MQUAKE\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003eSentiment\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/abs/2206.06520\" rel=\"nofollow\"\u003eConvsent\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003eBias\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/pdf/2304.00740.pdf\" rel=\"nofollow\"\u003eBias in Bios\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003eHallucination\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/Thartvigsen/GRACE/tree/main\"\u003eWikiBio\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003eCommonsense\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/abs/2305.14956\" rel=\"nofollow\"\u003eMEMIT\u003csub\u003ecsk\u003c/sub\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003econcept\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/abs/2403.06259\" rel=\"nofollow\"\u003eConceptEdit\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/2406.11614\" rel=\"nofollow\"\u003eCONCEPTVECTORS\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003eReasoning\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/pdf/2308.09954.pdf\" rel=\"nofollow\"\u003eEva-KELLM\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003ePrivacy Infomation Protect\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/abs/2310.02224\" rel=\"nofollow\"\u003ePrivQA\u003c/a\u003e, \u003ca href=\"https://arxiv.org/pdf/2309.11852.pdf\" rel=\"nofollow\"\u003eKnowledge Sanitation\u003c/a\u003e,\u003ca href=\"https://arxiv.org/pdf/2310.20138.pdf\" rel=\"nofollow\"\u003eEnron\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003eUnified Benchmark\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/pdf/2311.16087.pdf\" rel=\"nofollow\"\u003eDUnE\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003eToxic Information\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/abs/2009.11462\" rel=\"nofollow\"\u003eRealToxicityPrompts\u003c/a\u003e,\u003ca href=\"https://arxiv.org/pdf/2308.08090.pdf\" rel=\"nofollow\"\u003eToxicity Unlearning\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003eMultiModal\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/abs/2310.08475\" rel=\"nofollow\"\u003eMMEdit\u003c/a\u003e \u003ca href=\"https://arxiv.org/abs/2403.07350\" rel=\"nofollow\"\u003eVLKEB\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/2406.13219\" rel=\"nofollow\"\u003eMC-MKE\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTools\u003c/h3\u003e\u003ca id=\"user-content-tools\" class=\"anchor\" aria-label=\"Permalink: Tools\" href=\"#tools\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/zjunlp/EasyEdit\"\u003eEasyEdit\u003c/a\u003e: An Easy-to-use Knowledge Editing Framework for Large Language Models.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/hiyouga/FastEdit\"\u003eFastEdit\u003c/a\u003e: Editing large language models within 10 seconds\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePlease cite our paper if find our work useful.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-bibtex notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\n@article{zhang2024comprehensive,\n title={A Comprehensive Study of Knowledge Editing for Large Language Models},\n author={Zhang, Ningyu and Yao, Yunzhi and Tian, Bozhong and Wang, Peng and Deng, Shumin and Wang, Mengru and Xi, Zekun and Mao, Shengyu and Zhang, Jintian and Ni, Yuansheng and others},\n journal={arXiv preprint arXiv:2401.01286},\n year={2024}\n}\n\n@article{DBLP:journals/corr/abs-2305-13172,\n author = {Yunzhi Yao and\n Peng Wang and\n Bozhong Tian and\n Siyuan Cheng and\n Zhoubo Li and\n Shumin Deng and\n Huajun Chen and\n Ningyu Zhang},\n title = {Editing Large Language Models: Problems, Methods, and Opportunities},\n journal = {CoRR},\n volume = {abs/2305.13172},\n year = {2023},\n url = {https://doi.org/10.48550/arXiv.2305.13172},\n doi = {10.48550/arXiv.2305.13172},\n eprinttype = {arXiv},\n eprint = {2305.13172},\n timestamp = {Tue, 30 May 2023 17:04:46 +0200},\n biburl = {https://dblp.org/rec/journals/corr/abs-2305-13172.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n@inproceedings{wang2024knowledge,\n title={Knowledge Mechanisms in Large Language Models: A Survey and Perspective},\n author={Wang, Mengru and Yao, Yunzhi and Xu, Ziwen and Qiao, Shuofei and Deng, Shumin and Wang, Peng and Chen, Xiang and Gu, Jia-Chen and Jiang, Yong and Xie, Pengjun and others},\n booktitle={Findings of the Association for Computational Linguistics: EMNLP 2024},\n pages={7097--7135},\n year={2024}\n}\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@article\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003ezhang2024comprehensive\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eA Comprehensive Study of Knowledge Editing for Large Language Models\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eZhang, Ningyu and Yao, Yunzhi and Tian, Bozhong and Wang, Peng and Deng, Shumin and Wang, Mengru and Xi, Zekun and Mao, Shengyu and Zhang, Jintian and Ni, Yuansheng and others\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003ejournal\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003earXiv preprint arXiv:2401.01286\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2024\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e\n}\n\n\u003cspan class=\"pl-k\"\u003e@article\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003eDBLP:journals/corr/abs-2305-13172\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eYunzhi Yao and\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e Peng Wang and\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e Bozhong Tian and\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e Siyuan Cheng and\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e Zhoubo Li and\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e Shumin Deng and\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e Huajun Chen and\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e Ningyu Zhang\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eEditing Large Language Models: Problems, Methods, and Opportunities\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003ejournal\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eCoRR\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003evolume\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eabs/2305.13172\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2023\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eurl\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003ehttps://doi.org/10.48550/arXiv.2305.13172\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003edoi\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e10.48550/arXiv.2305.13172\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eeprinttype\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003earXiv\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eeprint\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2305.13172\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etimestamp\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eTue, 30 May 2023 17:04:46 +0200\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003ebiburl\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003ehttps://dblp.org/rec/journals/corr/abs-2305-13172.bib\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003ebibsource\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003edblp computer science bibliography, https://dblp.org\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e\n}\n\n\u003cspan class=\"pl-k\"\u003e@inproceedings\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003ewang2024knowledge\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eKnowledge Mechanisms in Large Language Models: A Survey and Perspective\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eWang, Mengru and Yao, Yunzhi and Xu, Ziwen and Qiao, Shuofei and Deng, Shumin and Wang, Peng and Chen, Xiang and Gu, Jia-Chen and Jiang, Yong and Xie, Pengjun and others\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003ebooktitle\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eFindings of the Association for Computational Linguistics: EMNLP 2024\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003epages\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e7097--7135\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2024\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e\n}\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🎉Contribution\u003c/h2\u003e\u003ca id=\"user-content-contribution\" class=\"anchor\" aria-label=\"Permalink: 🎉Contribution\" href=\"#contribution\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContributors\u003c/h3\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ca href=\"https://github.com/zjunlp/ModelEditingPapers/graphs/contributors\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/87e22514fae762eaa392183889816254829cad29321ae1e0c0bf3ca39cf6da1d/68747470733a2f2f636f6e747269622e726f636b732f696d6167653f7265706f3d7a6a756e6c702f4d6f64656c45646974696e67506170657273\" data-canonical-src=\"https://contrib.rocks/image?repo=zjunlp/ModelEditingPapers\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContributing to this paper list\u003c/h3\u003e\u003ca id=\"user-content-contributing-to-this-paper-list\" class=\"anchor\" aria-label=\"Permalink: Contributing to this paper list\" href=\"#contributing-to-this-paper-list\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThere are cases where we miss important works in this field, please contribute to this repo! Thanks for the efforts in advance.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAcknowledgement\u003c/h3\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Acknowledgement\" href=\"#acknowledgement\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eWe would like to express our gratitude to \u003ca href=\"https://yulonghui.github.io/\" rel=\"nofollow\"\u003eLonghui Yu\u003c/a\u003e for the kind reminder about the missing papers.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Knowledge Editing for LLMs Papers","anchor":"knowledge-editing-for-llms-papers","htmlText":"Knowledge Editing for LLMs Papers"},{"level":2,"text":"🔔 News","anchor":"-news","htmlText":"🔔 News"},{"level":2,"text":"🔍 Contents","anchor":"-contents","htmlText":"🔍 Contents"},{"level":2,"text":"🌟 Why Knowledge Editing?","anchor":"-why-knowledge-editing","htmlText":"🌟 Why Knowledge Editing?"},{"level":3,"text":"Keywords","anchor":"keywords","htmlText":"Keywords"},{"level":2,"text":"Comparisons of different technologies","anchor":"comparisons-of-different-technologies","htmlText":"Comparisons of different technologies"},{"level":2,"text":"📜 Resources","anchor":"-resources","htmlText":"📜 Resources"},{"level":3,"text":"Tutorials","anchor":"tutorials","htmlText":"Tutorials"},{"level":3,"text":"Surveys","anchor":"surveys","htmlText":"Surveys"},{"level":3,"text":"Methods","anchor":"methods","htmlText":"Methods"},{"level":4,"text":"Preserve Parameters","anchor":"preserve-parameters","htmlText":"Preserve Parameters"},{"level":5,"text":"Memory-based","anchor":"memory-based","htmlText":"Memory-based"},{"level":5,"text":"Additional Parameters","anchor":"additional-parameters","htmlText":"Additional Parameters"},{"level":5,"text":"Change LM's representation space","anchor":"change-lms-representation-space","htmlText":"Change LM's representation space"},{"level":4,"text":"Modify Parameters","anchor":"modify-parameters","htmlText":"Modify Parameters"},{"level":5,"text":"Finetuning","anchor":"finetuning","htmlText":"Finetuning"},{"level":5,"text":"Meta-learning","anchor":"meta-learning","htmlText":"Meta-learning"},{"level":5,"text":"Locate and edit","anchor":"locate-and-edit","htmlText":"Locate and edit"},{"level":4,"text":"More Related Papers","anchor":"more-related-papers","htmlText":"More Related Papers"},{"level":3,"text":"Analysis","anchor":"analysis","htmlText":"Analysis"},{"level":2,"text":"🧰 Resources","anchor":"-resources-1","htmlText":"🧰 Resources"},{"level":3,"text":"Benchmarks and Tasks","anchor":"benchmarks-and-tasks","htmlText":"Benchmarks and Tasks"},{"level":3,"text":"Tools","anchor":"tools","htmlText":"Tools"},{"level":2,"text":"Citation","anchor":"citation","htmlText":"Citation"},{"level":2,"text":"🎉Contribution","anchor":"contribution","htmlText":"🎉Contribution"},{"level":3,"text":"Contributors","anchor":"contributors","htmlText":"Contributors"},{"level":3,"text":"Contributing to this paper 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aria-describedby=":Rr9ab:-loading-announcement" id=":Rr9ab:"><svg aria-hidden="true" focusable="false" class="octicon octicon-list-unordered" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M5.75 2.5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Knowledge Editing for LLMs Papers</h1><a id="user-content-knowledge-editing-for-llms-papers" class="anchor" aria-label="Permalink: Knowledge Editing for LLMs Papers" href="#knowledge-editing-for-llms-papers"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://github.com/zjunlp/ModelEditingPapers"><img src="https://camo.githubusercontent.com/3418ba3754faddfb88c5cbdc94c31ad670fc693c8caa59bc2806c9836acc04e4/68747470733a2f2f617765736f6d652e72652f62616467652e737667" alt="Awesome" data-canonical-src="https://awesome.re/badge.svg" style="max-width: 100%;"></a> <a href="https://opensource.org/licenses/MIT" rel="nofollow"><img src="https://camo.githubusercontent.com/28f4d479bf0a9b033b3a3b95ab2adc343da448a025b01aefdc0fbc7f0e169eb8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d677265656e2e737667" alt="License: MIT" data-canonical-src="https://img.shields.io/badge/License-MIT-green.svg" style="max-width: 100%;"></a> <a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/108e193cfdc9a788229dfc1184064ec25065b5a3d5b064b842922faef004d198/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f7a6a756e6c702f4d6f64656c45646974696e675061706572733f636f6c6f723d677265656e"><img src="https://camo.githubusercontent.com/108e193cfdc9a788229dfc1184064ec25065b5a3d5b064b842922faef004d198/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f7a6a756e6c702f4d6f64656c45646974696e675061706572733f636f6c6f723d677265656e" alt="" data-canonical-src="https://img.shields.io/github/last-commit/zjunlp/ModelEditingPapers?color=green" style="max-width: 100%;"></a> <a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/30c6078aaee2b242e7c07f16316804464f124b8041003f717280157f42a411c0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f5052732d57656c636f6d652d726564"><img src="https://camo.githubusercontent.com/30c6078aaee2b242e7c07f16316804464f124b8041003f717280157f42a411c0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f5052732d57656c636f6d652d726564" alt="" data-canonical-src="https://img.shields.io/badge/PRs-Welcome-red" style="max-width: 100%;"></a></p> <p dir="auto">Must-read papers on <a href="https://arxiv.org/abs/2305.13172" rel="nofollow">knowledge editing</a> for large language models.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">🔔 News</h2><a id="user-content--news" class="anchor" aria-label="Permalink: 🔔 News" href="#-news"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>New Reports</strong></p> <markdown-accessiblity-table><table> <thead> <tr> <th align="center"><em>Report</em></th> <th align="center">Topic</th> <th align="center">PPT Resource</th> </tr> </thead> <tbody> <tr> <td align="center">NLPCC2024 tutorial</td> <td align="center">Knowledge Mechanism, Fusion, Editing for LLMs</td> <td align="center"><a href="https://drive.google.com/file/d/1MCzcOhK89bD2-b3s25Tb9cqDh8dEhIGt/view?usp=sharing" rel="nofollow">Google Drive</a></td> </tr> <tr> <td align="center">Invited Talk</td> <td align="center">Editing Large Language Models Advancing Machine Understanding and Control</td> <td align="center"><a href="https://drive.google.com/file/d/19t7BiCsiZx2c_wPZCGbH8BEwe5YSkJOX/view?usp=sharing" rel="nofollow">Google Drive</a></td> </tr> <tr> <td align="center">CCL2024 tutorial</td> <td align="center">大语言模型知识机理、融合与编辑</td> <td align="center"><a href="https://drive.google.com/file/d/1Tp7pWAl1MqMvNQUplPS411WQmmAmodcB/view?usp=sharing" rel="nofollow">Google Drive</a> & <a href="https://pan.baidu.com/s/14IFludc3KROrCdvSYrOfAw?pwd=shja" rel="nofollow">BaiduPan</a></td> </tr> <tr> <td align="center">IJCAI2024 tutorial</td> <td align="center">Knowledge Editing for Large Language Models</td> <td align="center"><a href="https://drive.google.com/file/d/1sg3Dy3gQo2bwOoLwWMFh57NfHROf3Ahg/view?usp=sharing" rel="nofollow">Google Drive</a></td> </tr> <tr> <td align="center">COLING2024 tutorial</td> <td align="center">Knowledge Editing for Large Language Models</td> <td align="center"><a href="https://drive.google.com/file/d/1vFzRYjnzkuZaNdjdIxQwWbEybCY7YqY9/view?usp=sharing" rel="nofollow">Google Drive</a></td> </tr> <tr> <td align="center">北京智源大会</td> <td align="center">大语言模型知识机理与编辑问题</td> <td align="center"><a href="https://pan.baidu.com/s/1X9n2LAkYYXFnPgkV_2b57Q?pwd=du6w" rel="nofollow">BaiduPan</a></td> </tr> <tr> <td align="center">VALSE2024 tutorial</td> <td align="center">Knowledge Mechanism and Editing for Large Language Models</td> <td align="center"><a href="https://drive.google.com/file/d/19T-InKopH-VHKAtphy9M6H366dXnenQX/view?usp=sharing" rel="nofollow">Google Drive</a></td> </tr> <tr> <td align="center">AAAI2024 tutorial</td> <td align="center">Knowledge Editing for Large Language Models</td> <td align="center"><a href="https://drive.google.com/file/d/1fkTbVeRJSWmU7fBDeNf1OhHEkLSofQde/view?usp=sharing" rel="nofollow">Google Drive</a></td> </tr> </tbody> </table></markdown-accessiblity-table> </li> </ul> <ul dir="auto"> <li><strong>2024-09-26 Our paper: "<a href="https://arxiv.org/abs/2405.14768" rel="nofollow">WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models</a>" and "<a href="https://arxiv.org/abs/2405.17969" rel="nofollow">Knowledge Circuits in Pretrained Transformers</a>" have been accepted by NeurIPS 2024. Our papers: "<a href="https://arxiv.org/abs/2407.15017" rel="nofollow">Knowledge Mechanisms in Large Language Models: A Survey and Perspective</a>" and "<a href="https://arxiv.org/abs/2403.06259" rel="nofollow">Editing Conceptual Knowledge for Large Language Models</a>" have been accepted by EMNLP 2024 Findings.</strong></li> <li><strong>2024-07-22 We release a new paper: "<a href="https://arxiv.org/abs/2407.15017" rel="nofollow">Knowledge Mechanisms in Large Language Models: A Survey and Perspective</a>", which reviews how knowledge is acquired, utilized, and evolves in large language models.</strong></li> <li><strong>2024-05-16 Our paper "<a href="https://arxiv.org/abs/2403.14472" rel="nofollow">Detoxifying Large Language Models via Knowledge Editing</a>" has been accepted by ACL 2024.</strong></li> <li><strong>2024-01-03 We release a new paper:"<a href="https://arxiv.org/abs/2401.01286" rel="nofollow">A Comprehensive Study of Knowledge Editing for Large Language Models</a>" with a new benchmark <a href="https://huggingface.co/datasets/zjunlp/KnowEdit" rel="nofollow">KnowEdit</a>! We are looking forward to any comments or discussions on this topic</strong></li> <li><strong>2024-12-09 Our paper "<a href="https://arxiv.org/abs/2301.10405" rel="nofollow">Editing Language Model-based Knowledge Graph Embeddings?</a>" has been accepted by AAAI 2024.</strong></li> <li><strong>2023-11-18 We will provide a tutorial on <em>Knowledge Editing for Large Language Models</em> at COLING 2024.</strong></li> <li><strong>2023-10-25 We will provide a tutorial on <em>Knowledge Editing for Large Language Models</em> at AAAI 2024.</strong></li> <li><strong>2023-10-22 Our paper "<a href="https://arxiv.org/abs/2310.08475" rel="nofollow">Can We Edit Multimodal Large Language Models?</a>" has been accepted by EMNLP 2023.</strong></li> <li><strong>2023-10-08 Our paper "<a href="https://arxiv.org/abs/2305.13172" rel="nofollow">Editing Large Language Models: Problems, Methods, and Opportunities</a>" has been accepted by EMNLP 2023.</strong></li> <li><strong>2023-8-15 We release the paper "<a href="https://arxiv.org/abs/2308.07269" rel="nofollow">EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models</a>."</strong></li> <li><strong>2023-07 We release <a href="https://github.com/zjunlp/EasyEdit">EasyEdit</a>, an easy-to-use knowledge editing framework for LLMs.</strong></li> <li><strong>2023-06 We will provide a tutorial on <em><a href="https://drive.google.com/file/d/1EW-cusC_llCM0wEshkIdYuYrvfBPCDRz/view?usp=sharing" rel="nofollow">Editing Large Language Models</a></em> at AACL 2023.</strong></li> <li><strong>2023-05 We release a new analysis paper:"<a href="https://arxiv.org/abs/2305.13172" rel="nofollow">Editing Large Language Models: Problems, Methods, and Opportunities</a>" based on this repository! We are looking forward to any comments or discussions on this topic :)</strong></li> <li><strong>2022-12 We create this repository to maintain a paper list on <em>Knowledge Editing</em>.</strong></li> </ul> <hr> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">🔍 Contents</h2><a id="user-content--contents" class="anchor" aria-label="Permalink: 🔍 Contents" href="#-contents"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="#-why-knowledge-editing">🌟 Why Knowledge Editing?</a></li> <li><a href="#keywords">Keywords</a></li> <li><a href="#comparisons-of-different-technologies">Comparisons of the different technologies</a></li> <li><a href="#-papers">📜 Papers</a> <ul dir="auto"> <li><a href="#overview">Overview</a></li> <li><a href="#methods">Methods</a> <ul dir="auto"> <li><a href="#preserve-parameters">Preserve Parameters</a> <ul dir="auto"> <li><a href="#memory-based">Memory Based</a></li> <li><a href="#additional-parameters">Additional Parameters</a></li> </ul> </li> <li><a href="#modify-parameters">Modify Parameters</a> <ul dir="auto"> <li><a href="#finetuning">Finetuning</a></li> <li><a href="#meta-learning">Meta-learning</a></li> <li><a href="#locate-and-edit">Locate and edit</a></li> </ul> </li> <li><a href="#more-related-papers">More Related Papers</a></li> </ul> </li> <li><a href="#analysis">Analysis</a></li> </ul> </li> <li><a href="#-resources">🧰 Resources</a> <ul dir="auto"> <li><a href="#benchmarks-and-tasks">Benchmarks and Tasks</a></li> <li><a href="#tools">Tools</a></li> </ul> </li> <li><a href="#contribution">🎉 Contribution</a> <ul dir="auto"> <li><a href="#contributors">Contributors</a></li> <li><a href="#contributing-to-this-paper-list">Contributing to this paper list</a></li> <li><a href="#acknowledgement">Acknowledgement</a></li> </ul> </li> <li><a href="#-citation">🚩Citation </a></li> </ul> <hr> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">🌟 Why Knowledge Editing?</h2><a id="user-content--why-knowledge-editing" class="anchor" aria-label="Permalink: 🌟 Why Knowledge Editing?" href="#-why-knowledge-editing"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Knowledge Editing is a compelling field of research that focuses on facilitating efficient modifications to the behavior of models, particularly <strong>foundation models</strong>. The aim is to implement these changes within a specified scope of interest without negatively affecting the model's performance across a broader range of inputs.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Keywords</h3><a id="user-content-keywords" class="anchor" aria-label="Permalink: Keywords" href="#keywords"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Knowledge Editing has strong connections with following topics.</p> <ul dir="auto"> <li>Updating and fixing bugs for large language models</li> <li>Language models as knowledge base, locating knowledge in large language models</li> <li>Lifelong learning, unlearning and etc.</li> <li>Security and privacy for large language models</li> </ul> <div align="center" dir="auto"><a target="_blank" rel="noopener noreferrer" href="/zjunlp/KnowledgeEditingPapers/blob/main/img/ke.png"><img src="/zjunlp/KnowledgeEditingPapers/raw/main/img/ke.png" width="100%" height="80%" style="max-width: 100%;"></a></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Comparisons of different technologies</h2><a id="user-content-comparisons-of-different-technologies" class="anchor" aria-label="Permalink: Comparisons of different technologies" href="#comparisons-of-different-technologies"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div align="center" dir="auto"><a target="_blank" rel="noopener noreferrer" href="/zjunlp/KnowledgeEditingPapers/blob/main/img/comparison.png"><img src="/zjunlp/KnowledgeEditingPapers/raw/main/img/comparison.png" width="60%" height="48%" style="max-width: 100%;"></a></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">📜 Resources</h2><a id="user-content--resources" class="anchor" aria-label="Permalink: 📜 Resources" href="#-resources"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This is a collection of research and review papers of Knowledge Editing. Any suggestions and pull requests are welcome for better sharing of latest research progress.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Tutorials</h3><a id="user-content-tutorials" class="anchor" aria-label="Permalink: Tutorials" href="#tutorials"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>Knowledge Editing for Large Language Models, AAAI 2024 Tutorial</strong> <br> Ningyu Zhang, Jia-Chen Gu, Yunzhi Yao, Zhen Bi, Shumin Deng. [<a href="https://github.com/zjunlp/KnowledgeEditingPapers/blob/main/AAAI2024%40Tutorial_Knowledge%20Editing%20for%20LLMs.pdf">Github</a>] [<a href="https://drive.google.com/file/d/1fkTbVeRJSWmU7fBDeNf1OhHEkLSofQde/view?usp=sharing" rel="nofollow">Google Drive</a>] [<a href="https://pan.baidu.com/s/1oJYgaMnxWIBE4kIcJuMSKg?pwd=p9j5" rel="nofollow">Baidu Pan</a>]</p> <p dir="auto"><strong>Editing Large Language Models, AACL 2023 Tutorial</strong> <br> Ningyu Zhang, Yunzhi Yao, Shumin Deng. [<a href="https://github.com/zjunlp/KnowledgeEditingPapers/blob/main/AACL2023%40Tutorial_Editing%20LLMs.pdf">Github</a>] [<a href="https://drive.google.com/file/d/1EW-cusC_llCM0wEshkIdYuYrvfBPCDRz/view?usp=sharing" rel="nofollow">Google Drive</a>] [<a href="https://pan.baidu.com/s/1NupastGJUzcUIAjI64J1tw?pwd=i5an" rel="nofollow">Baidu Pan</a>]</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Surveys</h3><a id="user-content-surveys" class="anchor" aria-label="Permalink: Surveys" href="#surveys"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>Knowledge Mechanisms in Large Language Models: A Survey and Perspective</strong> (EMNLP 2024 Findings) <br> Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang. [<a href="https://arxiv.org/abs/2407.15017" rel="nofollow">paper</a>]</p> <p dir="auto"><strong>A Comprehensive Study of Knowledge Editing for Large Language Models</strong> <br> Ningyu Zhang, Yunzhi Yao, Bozhong Tian, Peng Wang, Shumin Deng, Mengru Wang, Zekun Xi, Shengyu Mao, Jintian Zhang, Yuansheng Ni, Siyuan Cheng, Ziwen Xu, Xin Xu, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Lei Liang, Zhiqiang Zhang, Xiaowei Zhu, Jun Zhou, Huajun Chen. [<a href="https://arxiv.org/abs/2401.01286" rel="nofollow">paper</a>][<a href="https://huggingface.co/datasets/zjunlp/KnowEdit" rel="nofollow">benchmark</a>][<a href="https://github.com/zjunlp/EasyEdit">code</a>]</p> <p dir="auto"><strong>Editing Large Language Models: Problems, Methods, and Opportunities, EMNLP 2023 Main Conference Paper</strong> <br> Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang. [<a href="https://arxiv.org/abs/2305.13172" rel="nofollow">paper</a>][<a href="https://github.com/zjunlp/EasyEdit">code</a>]</p> <p dir="auto"><strong>Knowledge Editing for Large Language Models: A Survey</strong> <br> Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, Jundong Li. [<a href="https://arxiv.org/abs/2310.16218" rel="nofollow">paper</a>]</p> <p dir="auto"><strong>A Survey on Knowledge Editing of Neural Networks</strong> <br> Vittorio Mazzia, Alessandro Pedrani, Andrea Caciolai, Kay Rottmann, Davide Bernardi. [<a href="https://arxiv.org/abs/2310.19704" rel="nofollow">paper</a>]</p> <p dir="auto"><strong>Knowledge Unlearning for LLMs: Tasks, Methods, and Challenges</strong> <br> Nianwen Si, Hao Zhang, Heyu Chang, Wenlin Zhang, Dan Qu, Weiqiang Zhang. [<a href="https://arxiv.org/abs/2311.15766" rel="nofollow">paper</a>]</p> <div align="center" dir="auto"><a target="_blank" rel="noopener noreferrer" href="/zjunlp/KnowledgeEditingPapers/blob/main/img/overview.jpg"><img src="/zjunlp/KnowledgeEditingPapers/raw/main/img/overview.jpg" width="100%" height="80%" style="max-width: 100%;"></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Methods</h3><a id="user-content-methods" class="anchor" aria-label="Permalink: Methods" href="#methods"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Preserve Parameters</h4><a id="user-content-preserve-parameters" class="anchor" aria-label="Permalink: Preserve Parameters" href="#preserve-parameters"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">Memory-based</h5><a id="user-content-memory-based" class="anchor" aria-label="Permalink: Memory-based" href="#memory-based"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li> <p dir="auto"><strong>Memory-Based Model Editing at Scale</strong> (ICML 2022) <br> Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D. Manning, Chelsea Finn. [<a href="https://arxiv.org/abs/2206.06520" rel="nofollow">paper</a>] [<a href="https://github.com/eric-mitchell/serac">code</a>] [<a href="https://sites.google.com/view/serac-editing" rel="nofollow">demo</a>]</p> </li> <li> <p dir="auto"><strong>Fixing Model Bugs with Natural Language Patches</strong>. (EMNLP 2022) <br> Shikhar Murty, Christopher D. Manning, Scott M. Lundberg, Marco Túlio Ribeiro. [<a href="https://arxiv.org/abs/2211.03318" rel="nofollow">paper</a>] [<a href="https://github.com/MurtyShikhar/LanguagePatching">code</a>]</p> </li> <li> <p dir="auto"><strong>MemPrompt: Memory-assisted Prompt Editing with User Feedback</strong>. (EMNLP 2022) <br> Aman Madaan, Niket Tandon, Peter Clark, Yiming Yang. [<a href="https://arxiv.org/abs/2201.06009" rel="nofollow">paper</a>] [<a href="https://github.com/madaan/memprompt">code</a>] [<a href="https://memprompt.com/" rel="nofollow">page</a>] [<a href="https://www.youtube.com/watch?v=Ld7R02bOiNQ&t=1s" rel="nofollow">video</a>]</p> </li> <li> <p dir="auto"><strong>Large Language Models with Controllable Working Memory</strong>. <br> Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix Yu, Sanjiv Kumar. [<a href="https://arxiv.org/abs/2211.05110" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Can We Edit Factual Knowledge by In-Context Learning?</strong> <br> Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, Baobao Chang. [<a href="https://arxiv.org/abs/2305.12740" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Can LMs Learn New Entities from Descriptions? Challenges in Propagating Injected Knowledge</strong> <br> Yasumasa Onoe, Michael J.Q. Zhang, Shankar Padmanabhan, Greg Durrett, Eunsol Choi. [<a href="https://arxiv.org/abs/2305.01651" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>MQUAKE: Assessing Knowledge Editing inLanguage Models via Multi-Hop Questions</strong> <br> Zexuan Zhong, Zhengxuan Wu, Christopher D. Manning, Christopher Potts, Danqi Chen.<br>[<a href="https://arxiv.org/abs/2305.14795" rel="nofollow">paper</a>] [<a href="https://github.com/princeton-nlp/MQuAKE">code</a>]</p> </li> <li> <p dir="auto"><strong>PokeMQA: Programmable knowledge editing for Multi-hop Question Answering</strong> <br> Hengrui Gu, Kaixiong Zhou, Xiaotian Han, Ninghao Liu, Ruobing Wang, Xin Wang. <br> [<a href="https://arxiv.org/abs/2312.15194" rel="nofollow">paper</a>] [<a href="https://github.com/Hengrui-Gu/PokeMQA">code</a>]</p> </li> <li> <p dir="auto"><strong>Retrieval-augmented Multilingual Knowledge Editing</strong> <br> Weixuan Wang, Barry Haddow, Alexandra Birch. [<a href="https://arxiv.org/abs/2312.13040" rel="nofollow">paper</a>] [<a href="https://github.com/Vicky-Wil/ReMaKE">code</a>]</p> </li> <li> <p dir="auto"><strong>MEMORYLLM: Towards Self-Updatable Large Language Models</strong> <br> Yu Wang, Xiusi Chen, Jingbo Shang, Julian McAuley. [<a href="https://arxiv.org/abs/2402.04624" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>DeepEdit: Knowledge Editing as Decoding with Constraints</strong> <br> Yiwei Wang,Muhao Chen,Nanyun Peng, Kai-Wei Chang. [<a href="https://arxiv.org/abs/2401.10471" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Stable Knowledge Editing in Large Language Models</strong>. <br> Zihao Wei,Liang Pang,Hanxing Ding,Jingcheng Deng,Huawei Shen,Xueqi Cheng. [<a href="https://arxiv.org/abs/2402.13048" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Knowledge Editing on Black-box Large Language Models</strong>. <br> Xiaoshuai Song, Zhengyang Wang, Keqing He, Guanting Dong, Jinxu Zhao, Weiran Xu. [<a href="https://arxiv.org/abs/2402.08631" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Learning to Edit: Aligning LLMs with Knowledge Editing</strong>. <br> Yuxin Jiang, Yufei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu, Wei Wang. [<a href="https://arxiv.org/abs/2402.11905" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Robust and Scalable Model Editing for Large Language Models</strong>. <br> Yingfa Chen, Zhengyan Zhang, Xu Han, Chaojun Xiao, Zhiyuan Liu, Chen Chen, Kuai Li, Tao Yang, Maosong Sun. [<a href="https://arxiv.org/abs/2403.17431" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Retrieval-Enhanced Knowledge Editing for Multi-Hop Question Answering in Language Models</strong>. <br> Yucheng Shi, Qiaoyu Tan, Xuansheng Wu, Shaochen Zhong, Kaixiong Zhou, Ninghao Liu. [<a href="https://arxiv.org/abs/2403.19631" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>In-Context Editing: Learning Knowledge from Self-Induced Distributions</strong>. <br> Siyuan Qi, Bangcheng Yang, Kailin Jiang, Xiaobo Wang, Jiaqi Li, Yifan Zhong, Yaodong Yang, Zilong Zheng. [<a href="https://arxiv.org/abs/2406.11194" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Cross-Lingual Multi-Hop Knowledge Editing</strong>. <br> Aditi Khandelwal, Harman Singh, Hengrui Gu, Tianlong Chen, Kaixiong Zhou. [<a href="https://arxiv.org/abs/2407.10275" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>AnyEdit: Edit Any Knowledge Encoded in Language Models</strong>. <br> Houcheng Jiang, Junfeng Fang, Ningyu Zhang, Guojun Ma, Mingyang Wan, Xiang Wang, Xiangnan He, Tat-seng Chua. [<a href="https://arxiv.org/abs/2502.05628" rel="nofollow">paper</a>]</p> </li> </ol> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">Additional Parameters</h5><a id="user-content-additional-parameters" class="anchor" aria-label="Permalink: Additional Parameters" href="#additional-parameters"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li> <p dir="auto"><strong>Calibrating Factual Knowledge in Pretrained Language Models</strong>. (EMNLP 2022) <br> Qingxiu Dong, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, Lei Li. [<a href="https://arxiv.org/abs/2210.03329" rel="nofollow">paper</a>] [<a href="https://github.com/dqxiu/CaliNet">code</a>]</p> </li> <li> <p dir="auto"><strong>Transformer-Patcher: One Mistake worth One Neuron</strong>. (ICLR 2023) <br> Zeyu Huang, Yikang Shen, Xiaofeng Zhang, Jie Zhou, Wenge Rong, Zhang Xiong. [<a href="https://arxiv.org/abs/2301.09785" rel="nofollow">paper</a>] [<a href="https://github.com/ZeroYuHuang/Transformer-Patcher">code</a>]</p> </li> <li> <p dir="auto"><strong>Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors</strong>. (NeurIPS 2023) <br> Thomas Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, Marzyeh Ghassemi. [<a href="https://arxiv.org/abs/2211.11031" rel="nofollow">paper</a>] [<a href="https://github.com/thartvigsen/grace">code</a>]</p> </li> <li> <p dir="auto"><strong>Neural Knowledge Bank for Pretrained Transformers</strong> <br> Damai Dai, Wenbin Jiang, Qingxiu Dong, Yajuan Lyu, Qiaoqiao She, Zhifang Sui. [<a href="http://arxiv.org/abs/2208.00399" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Rank-One Editing of Encoder-Decoder Models</strong> <br> Vikas Raunak, Arul Menezes. [<a href="https://arxiv.org/abs/2211.13317" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>MELO: Enhancing Model Editing with Neuron-Indexed Dynamic LoRA.</strong> (AAAI 2024) <br> Lang Yu, Qin Chen, Jie Zhou, Liang He. [<a href="https://arxiv.org/abs/2312.11795" rel="nofollow">paper</a>] [<a href="https://github.com/BruthYU/MELO">code</a>]</p> </li> <li> <p dir="auto"><strong>MPN: Leveraging Multilingual Patch Neuron for Cross-lingual Model Editing</strong> <br> Nianwen Si, Hao Zhang, Weiqiang Zhang. [<a href="https://arxiv.org/abs/2401.03190" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>SWEA: Changing Factual Knowledge in Large Language Models via Subject Word Embedding Altering</strong> <br> Xiaopeng Li, Shasha Li, Bin Ji, Shezheng Song. [<a href="https://arxiv.org/abs/2401.17809" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>MEMoE: Enhancing Model Editing with Mixture of Experts Adaptors</strong> <br> Renzhi Wang, Piji Li. [<a href="https://arxiv.org/abs/2405.19086" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models</strong>. (NeurIPS 2024) <br> Peng Wang, Zexi Li, Ningyu Zhang, Ziwen Xu, Yunzhi Yao, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen. [<a href="https://arxiv.org/abs/2405.14768" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>MEMLA: Enhancing Multilingual Knowledge Editing with Neuron-Masked Low-Rank Adaptation</strong>. <br> Jiakuan Xie, Pengfei Cao, Yuheng Chen, Yubo Chen, Kang Liu, Jun Zhao. [<a href="https://arxiv.org/abs/2405.14768" rel="nofollow">paper</a>]</p> </li> </ol> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">Change LM's representation space</h5><a id="user-content-change-lms-representation-space" class="anchor" aria-label="Permalink: Change LM's representation space" href="#change-lms-representation-space"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li><strong>Inspecting and Editing Knowledge Representations in Language Models</strong> <br> Evan Hernandez, Belinda Z. Li, Jacob Andreas. [<a href="http://arxiv.org/abs/2304.00740" rel="nofollow">paper</a>] [<a href="https://github.com/evandez/REMEDI">code</a>]</li> </ol> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Modify Parameters</h4><a id="user-content-modify-parameters" class="anchor" aria-label="Permalink: Modify Parameters" href="#modify-parameters"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">Finetuning</h5><a id="user-content-finetuning" class="anchor" aria-label="Permalink: Finetuning" href="#finetuning"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li> <p dir="auto"><strong>Plug-and-Play Adaptation for Continuously-updated QA</strong>. (ACL 2022 Findings) <br> Kyungjae Lee, Wookje Han, Seung-won Hwang, Hwaran Lee, Joonsuk Park, Sang-Woo Lee. [<a href="https://arxiv.org/abs/2204.12785" rel="nofollow">paper</a>] [<a href="https://github.com/wookjeHan/Plug-and-Play-Adaptation-for-Continuously-updated-QA">code</a>]</p> </li> <li> <p dir="auto"><strong>Modifying Memories in Transformer Models</strong>. <br> Chen Zhu, Ankit Singh Rawat, Manzil Zaheer, Srinadh Bhojanapalli, Daliang Li, Felix Yu, Sanjiv Kumar. [<a href="https://arxiv.org/abs/2012.00363" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models</strong> <br> Shiwen Ni, Dingwei Chen, Chengming Li, Xiping Hu, Ruifeng Xu and Min Yang. [<a href="https://arxiv.org/pdf/2311.08011.pdf" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>LLM Surgery: Efficient Knowledge Unlearning and Editing in Large Language Models</strong> <br> Akshaj Kumar Veldanda, Shi-Xiong Zhang, Anirban Das, Supriyo Chakraborty, Stephen Rawls, Sambit Sahu, Milind Naphade. [<a href="https://arxiv.org/abs/2409.13054" rel="nofollow">paper</a>]</p> </li> </ol> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">Meta-learning</h5><a id="user-content-meta-learning" class="anchor" aria-label="Permalink: Meta-learning" href="#meta-learning"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li> <p dir="auto"><strong>Editing Factual Knowledge in Language Models</strong>. (EMNLP 2021) <br> Nicola De Cao, Wilker Aziz, Ivan Titov. [<a href="https://arxiv.org/abs/2104.08164" rel="nofollow">paper</a>] [<a href="https://github.com/nicola-decao/KnowledgeEditor">code</a>]</p> </li> <li> <p dir="auto"><strong>Fast Model Editing at Scale</strong>. (ICLR 2022) <br> Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, Christopher D. Manning. [<a href="https://arxiv.org/abs/2110.11309" rel="nofollow">paper</a>] [<a href="https://github.com/eric-mitchell/mend">code</a>] [<a href="https://sites.google.com/view/mend-editing" rel="nofollow">page</a>]</p> </li> <li> <p dir="auto"><strong>Editable Neural Networks</strong>. (ICLR 2020) <br> Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitry V. Pyrkin, Sergei Popov, Artem Babenko. [<a href="https://arxiv.org/abs/2004.00345" rel="nofollow">paper</a>] [<a href="https://github.com/xtinkt/editable">code</a>]</p> </li> <li> <p dir="auto"><strong>Editing Language Model-based Knowledge Graph Embeddings</strong> (AAAI 2024) <br> Siyuan Cheng, Ningyu Zhang, Bozhong Tian, Xi Chen, Qingbing Liu, Huajun Chen. [<a href="https://arxiv.org/abs/2301.10405" rel="nofollow">paper</a>] [<a href="https://github.com/zjunlp/PromptKG/tree/main/deltaKG">code</a>]</p> </li> <li> <p dir="auto"><strong>Massive Editing for Large Language Model via Meta Learning.</strong> (ICLR 2024) <br> Chenmien Tan1, Ge Zhang, Jie Fu. [<a href="https://arxiv.org/pdf/2311.04661.pdf" rel="nofollow">paper</a>] [<a href="https://github.com/ChenmienTan/malmen">code</a>]</p> </li> </ol> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">Locate and edit</h5><a id="user-content-locate-and-edit" class="anchor" aria-label="Permalink: Locate and edit" href="#locate-and-edit"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li> <p dir="auto"><strong>Editing a classifier by rewriting its prediction rules</strong>. (NeurIPS 2021) <br> Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, Aleksander Madry. [<a href="https://proceedings.neurips.cc/paper/2021/hash/c46489a2d5a9a9ecfc53b17610926ddd-Abstract.html" rel="nofollow">paper</a>] [<a href="https://github.com/MadryLab/EditingClassifiers">code</a>]</p> </li> <li> <p dir="auto"><strong>Language Anisotropic Cross-Lingual Model Editing</strong>. <br> Yang Xu, Yutai Hou, Wanxiang Che. [<a href="https://arxiv.org/abs/2205.12677" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Repairing Neural Networks by Leaving the Right Past Behind</strong>. <br> Ryutaro Tanno, Melanie F. Pradier, Aditya Nori, Yingzhen Li. [<a href="https://arxiv.org/abs/2207.04806" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Locating and Editing Factual Associations in GPT</strong>. (NeurIPS 2022) <br> Kevin Meng, David Bau, Alex Andonian, Yonatan Belinkov. [<a href="https://arxiv.org/abs/2202.05262" rel="nofollow">paper</a>] [<a href="https://github.com/kmeng01/rome">code</a>] [<a href="https://rome.baulab.info/" rel="nofollow">page</a>] [<a href="https://www.youtube.com/watch?v=_NMQyOu2HTo&t=0" rel="nofollow">video</a>]</p> </li> <li> <p dir="auto"><strong>Mass-Editing Memory in a Transformer</strong>. (ICLR 2023) <br> Kevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, David Bau. [<a href="https://arxiv.org/abs/2210.07229" rel="nofollow">paper</a>] [<a href="https://github.com/kmeng01/memit">code</a>] [<a href="https://memit.baulab.info/" rel="nofollow">page</a>] [<a href="https://memit.baulab.us/#/" rel="nofollow">demo</a>]</p> </li> <li> <p dir="auto"><strong>Editing models with task arithmetic</strong>. (ICLR 2023) <br> Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, Ali Farhadi. [<a href="https://openreview.net/pdf?id=6t0Kwf8-jrj" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Editing Common Sense in Transformers</strong>. (EMNLP 2023) <br> Anshita Gupta, Debanjan Mondal, Akshay Krishna Sheshadri, Wenlong Zhao, Xiang Lorraine Li, Sarah Wiegreffe, Niket Tandon. [<a href="https://arxiv.org/abs/2305.14956" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs</strong>. (EACL 2023) <br> Peter Hase, Mona Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, Srinivasan Iyer. [<a href="https://arxiv.org/pdf/2111.13654.pdf" rel="nofollow">paper</a>] [<a href="https://github.com/peterbhase/SLAG-Belief-Updating">code</a>]</p> </li> <li> <p dir="auto"><strong>Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark</strong>. (ACL 2023 Findings)<br> Jason Hoelscher-Obermaier, Julia Persson, Esben Kran, Ioannis Konstas, Fazl Barez. [<a href="https://arxiv.org/abs/2305.17553" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Knowledge Neurons in Pretrained Transformers</strong>.(ACL 2022) <br> Damai Dai , Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, Furu Wei.[<a href="http://arxiv.org/abs/2104.08696" rel="nofollow">paper</a>] [<a href="https://github.com/Hunter-DDM/knowledge-neurons">code</a>] [<a href="https://github.com/EleutherAI/knowledge-neurons">code by EleutherAI</a>]</p> </li> <li> <p dir="auto"><strong>LEACE: Perfect linear concept erasure in closed form</strong> .<br> Nora Belrose, David Schneider-Joseph, Shauli Ravfogel, Ryan Cotterell, Edward Raff, Stella Biderman. [<a href="https://arxiv.org/abs/2306.03819" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Transformer Feed-Forward Layers Are Key-Value Memories</strong>. (EMNLP 2021) <br> Mor Geva, Roei Schuster, Jonathan Berant, Omer Levy. [<a href="https://arxiv.org/abs/2012.14913" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space</strong>.(EMNLP 2022) <br> Mor Geva, Avi Caciularu, Kevin Ro Wang, Yoav Goldberg. [<a href="https://arxiv.org/abs/2203.14680" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>PMET: Precise Model Editing in a Transformer.</strong> (AAAI 2024) <br> Xiaopeng Li, Shasha Li, Shezheng Song, Jing Yang, Jun Ma, Jie Yu. [<a href="https://arxiv.org/abs/2308.08742" rel="nofollow">paper</a>] [<a href="https://github.com/xpq-tech/PMET.git">code</a>]</p> </li> <li> <p dir="auto"><strong>Unlearning Bias in Language Models by Partitioning Gradients.</strong> (ACL 2023 Findings) <br> Charles Yu, Sullam Jeoung, Anish Kasi, Pengfei Yu, Heng Ji. [<a href="https://aclanthology.org/2023.findings-acl.375.pdf" rel="nofollow">paper</a>] [<a href="https://github.com/CharlesYu2000/PCGU-UnlearningBias">code</a>]</p> </li> <li> <p dir="auto"><strong>DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models</strong> (EMNLP 2023) <br> Xinwei Wu, Junzhuo Li, Minghui Xu, Weilong Dong, Shuangzhi Wu, Chao Bian, Deyi Xiong. [<a href="https://arxiv.org/pdf/2310.20138.pdf" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Untying the Reversal Curse via Bidirectional Language Model Editing</strong> <br> Jun-Yu Ma, Jia-Chen Gu, Zhen-Hua Ling, Quan Liu, Cong Liu. [<a href="https://arxiv.org/pdf/2310.10322.pdf" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Trace and Edit Relation Associations in GPT</strong> <br> Jiahang Li,Taoyu Chen,Yuanli Wang. [<a href="https://arxiv.org/abs/2401.02976" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Consecutive Model Editing with Batch alongside HooK Layers</strong> <br> Shuaiyi Li,Yang Deng,Deng Cai,Hongyuan Lu,Liang Chen,Wai Lam. [<a href="https://arxiv.org/abs/2403.05330" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>A Unified Framework for Model Editing</strong> <br> Akshat Gupta,Dev Sajnani,Gopala Anumanchipalli. [<a href="https://arxiv.org/abs/2403.14236" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Detoxifying Large Language Models via Knowledge Editing</strong> <br> Mengru Wang, Ningyu Zhang, Ziwen Xu, Zekun Xi, Shumin Deng, Yunzhi Yao, Qishen Zhang, Linyi Yang, Jindong Wang, Huajun Chen. [<a href="https://arxiv.org/abs/2403.14472" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Locating and Editing Factual Associations in Mamba</strong> <br> Arnab Sen Sharma,David Atkinson,David Bau. [<a href="https://arxiv.org/abs/2404.03646" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Large Language Model Bias Mitigation from the Perspective of Knowledge Editing</strong> <br> Ruizhe Chen, Yichen Li, Zikai Xiao, Zuozhu Liu. [<a href="https://arxiv.org/abs/2405.09341" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>WilKE: Wise-Layer Knowledge Editor for Lifelong Knowledge Editing</strong> <br> Chenhui Hu, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao. [<a href="https://arxiv.org/abs/2402.10987" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>ReFACT: Updating Text-to-Image Models by Editing the Text Encoder</strong> <br> Dana Arad, Hadas Orgad, Yonatan Belinkov. [<a href="https://arxiv.org/pdf/2303.08084" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Editing Implicit Assumptions in Text-to-Image Diffusion Models</strong> <br> Hadas Orgad, Bahjat Kawar, Yonatan Belinkov. [<a href="https://arxiv.org/pdf/2303.08084" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Mass-Editing Memory with Attention in Transformers: A cross-lingual exploration of knowledge</strong> <br> Daniel Tamayo, Aitor Gonzalez-Agirre, Javier Hernando, Marta Villegas. [<a href="https://arxiv.org/abs/2502.02173" rel="nofollow">paper</a>]</p> </li> </ol> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">More Related Papers</h4><a id="user-content-more-related-papers" class="anchor" aria-label="Permalink: More Related Papers" href="#more-related-papers"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li> <p dir="auto"><strong>FRUIT: Faithfully Reflecting Updated Information in Text</strong>. (NAACL 2022) <br> Robert L. Logan IV, Alexandre Passos, Sameer Singh, Ming-Wei Chang. [<a href="https://arxiv.org/pdf/2112.08634" rel="nofollow">paper</a>] [<a href="https://github.com/google-research/language/tree/master/language/fruit">code</a>]</p> </li> <li> <p dir="auto"><strong>Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning</strong>. (EMNLP 2022) <br> Oyvind Tafjord, Bhavana Dalvi Mishra, Peter Clark. [<a href="https://arxiv.org/abs/2210.12217" rel="nofollow">paper</a>] [<a href="https://github.com/allenai/entailment_bank">code</a>] [<a href="https://www.youtube.com/watch?v=GYTJ_Pxva7Q" rel="nofollow">video</a>]</p> </li> <li> <p dir="auto"><strong>Towards Tracing Factual Knowledge in Language Models Back to the Training Data</strong>. <br> Ekin Akyürek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong, Ian Tenney, Jacob Andreas, Kelvin Guu. (EMNLP 2022) [<a href="https://arxiv.org/abs/2204.12785" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Prompting GPT-3 To Be Reliable</strong>. <br> Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan Boyd-Graber, Lijuan Wang. [<a href="https://arxiv.org/abs/2210.09150" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Patching open-vocabulary models by interpolating weights</strong>. (NeurIPS 2022) <br> Gabriel Ilharco, Mitchell Wortsman, Samir Yitzhak Gadre, Shuran Song, Hannaneh Hajishirzi, Simon Kornblith, Ali Farhadi, Ludwig Schmidt. [<a href="https://arxiv.org/abs/2208.05592" rel="nofollow">paper</a>] [<a href="https://github.com/mlfoundations/patching">code</a>]</p> </li> <li> <p dir="auto"><strong>Decouple knowledge from paramters for plug-and-play language modeling</strong> (ACL2023 Findings) <br> Xin Cheng, Yankai Lin, Xiuying Chen, Dongyan Zhao, Rui Yan.[<a href="http://arxiv.org/abs/2305.11564" rel="nofollow">paper</a>] [<a href="https://github.com/Hannibal046/PlugLM">code</a>]</p> </li> <li> <p dir="auto"><strong>Backpack Language Models</strong> <br> John Hewitt, John Thickstun, Christopher D. Manning, Percy Liang. [<a href="https://arxiv.org/pdf/2305.16765.pdf" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Learning to Model Editing Processes</strong>. (EMNLP 2022) <br> Machel Reid, Graham Neubig. [<a href="https://aclanthology.org/2022.findings-emnlp.280.pdf" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications</strong>. <br> Zhangyin Feng, Weitao Ma, Weijiang Yu, Lei Huang, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, Ting liu. [<a href="https://arxiv.org/pdf/2311.05876.pdf" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>DUnE: Dataset for Unified Editing</strong>. (EMNLP 2023) <br> Afra Feyza Akyürek, Eric Pan, Garry Kuwanto, Derry Wijaya. [<a href="https://arxiv.org/abs/2311.16087" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>See the Unseen: Better Context-Consistent Knowledge-Editing by Noises</strong>. <br> Youcheng Huang, Wenqiang Lei, Zheng Zhang, Jiancheng Lv, Shuicheng Yan. [<a href="https://arxiv.org/abs/2401.07544" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Sowing the Wind, Reaping the Whirlwind: The Impact of Editing Language Models</strong>. <br> Rima Hazra, Sayan Layek, Somnath Banerjee, Soujanya Poria. [<a href="https://arxiv.org/pdf/2401.10647.pdf" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Model Editing with Canonical Examples</strong>. <br> John Hewitt, Sarah Chen, Lanruo Lora Xie. [<a href="https://arxiv.org/pdf/2402.06155.pdf" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>EVEDIT: Event-based Knowledge Editing with Deductive Editing Boundaries</strong>. <br> Jiateng Liu,Pengfei Yu,Yuji Zhang,Sha Li,Zixuan Zhang,Heng Ji. [<a href="https://arxiv.org/abs/2402.11324" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Investigating Multi-Hop Factual Shortcuts in Knowledge Editing of Large Language Models</strong>. <br> Tianjie Ju,Yijin Chen,Xinwei Yuan,Zhuosheng Zhang,Wei Du,Yubin Zheng,Gongshen Liu. [<a href="https://arxiv.org/abs/2402.11900" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Knowledge Graph Enhanced Large Language Model Editing</strong>. <br> Mengqi Zhang,Xiaotian Ye,Qiang Liu,Pengjie Ren,Shu Wu,Zhumin Chen. [<a href="https://arxiv.org/abs/2402.13593" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models</strong>. <br> Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin. [<a href="https://arxiv.org/abs/2402.18099" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>KEBench: A Benchmark on Knowledge Editing for Large Vision-Language Models</strong>. <br> Han Huang, Haitian Zhong, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. [<a href="https://arxiv.org/pdf/2403.07350.pdf" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>COLLABEDIT: TOWARDS NON-DESTRUCTIVE COLLABORATIVE KNOWLEDGE EDITING</strong>. <br> Jiamu Zheng, Jinghuai Zhang, Futing Wang, Tianyu Du, Tao Lin. [<a href="https://openreview.net/pdf?id=PoVpff2Udw" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>TAXI: Evaluating Categorical Knowledge Editing for Language Models</strong>. <br> Derek Powell, Walter Gerych, Thomas Hartvigsen. [<a href="https://arxiv.org/pdf/2404.15004" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Large Scale Knowledge Washing</strong>. <br> Yu Wang, Ruihan Wu, Zexue He, Xiusi Chen, Julian McAuley. [<a href="https://arxiv.org/abs/2405.16720" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Defending Large Language Models Against Jailbreak Attacks via Layer-specific Editing</strong>. <br> Wei Zhao, Zhe Li, Yige Li, Ye Zhang, Jun Sun. [<a href="https://arxiv.org/abs/2406.02882" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Outdated Issue Aware Decoding for Factual Knowledge Editing</strong>. <br> Zengkui Sun, Yijin Liu, Jiaan Wang, Fandong Meng, Jinan Xu, Yufeng Chen, Jie Zhou. [<a href="https://arxiv.org/abs/2405.18166" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Adaptive Token Biaser: Knowledge Editing via Biasing Key Entities</strong>. <br> Baolong Bi, Shenghua Liu, Yiwei Wang, Lingrui Mei, Hongcheng Gao, Yilong Xu, Xueqi Cheng. [<a href="https://arxiv.org/abs/2406.12468" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Language Modeling with Editable External Knowledge</strong>. <br> Belinda Z. Li, Emmy Liu, Alexis Ross, Abbas Zeitoun, Graham Neubig, Jacob Andreas. [<a href="https://arxiv.org/abs/2406.11830" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Safety Arithmetic: A Framework for Test-time Safety Alignment of Language Models by Steering Parameters and Activations</strong>. <br> Rima Hazra, Sayan Layek, Somnath Banerjee, Soujanya Poria. [<a href="https://arxiv.org/abs/2406.11801" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Enhancing Data Privacy in Large Language Models through Private Association Editing</strong>. <br> Kunquan Deng, Zeyu Huang, Chen Li, Chenghua Lin, Min Gao, Wenge Rong. [<a href="https://arxiv.org/abs/2407.00488" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>PFME: A Modular Approach for Fine-grained Hallucination Detection and Editing of Large Language Models</strong>. <br> Davide Venditti, Elena Sofia Ruzzetti, Giancarlo A. Xompero, Cristina Giannone, Andrea Favalli, Raniero Romagnoli, Fabio Massimo Zanzotto. [<a href="https://arxiv.org/abs/2406.18221" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>SafeInfer: Context Adaptive Decoding Time Safety Alignment for Large Language Models</strong>. <br> Somnath Banerjee, Soham Tripathy, Sayan Layek, Shanu Kumar, Animesh Mukherjee, Rima Hazra. [<a href="https://arxiv.org/abs/2406.12274" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Editing Implicit Assumptions in Text-to-Image Diffusion Models</strong>. (ICCV 2023) <br> Hadas Orgad, Bahjat Kawar, Yonatan Belinkov. [<a href="https://arxiv.org/abs/2303.08084" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>ReFACT: Updating Text-to-Image Models by Editing the Text Encoder</strong>. (NAAC 2024) <br> Dana Arad, Hadas Orgad, Yonatan Belinkov. [<a href="https://arxiv.org/abs/2306.00738" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>MC-MKE: A Fine-Grained Multimodal Knowledge Editing Benchmark Emphasizing Modality Consistency</strong>. <br> Junzhe Zhang, Huixuan Zhang, Xunjian Yin, Baizhou Huang, Xu Zhang, Xinyu Hu, Xiaojun Wan. [<a href="https://arxiv.org/abs/2406.13219" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>LLM-Based Multi-Hop Question Answering with Knowledge Graph Integration in Evolving Environments</strong>. <br> Ruirui Chen, Weifeng Jiang, Chengwei Qin, Ishaan Singh Rawal, Cheston Tan, Dongkyu Choi, Bo Xiong, Bo Ai. [<a href="https://arxiv.org/abs/2408.15903" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Pioneering Reliable Assessment in Text-to-Image Knowledge Editing: Leveraging a Fine-Grained Dataset and an Innovative Criterion</strong>. <br> Hengrui Gu, Kaixiong Zhou, Yili Wang, Ruobing Wang, Xin Wang. [<a href="https://arxiv.org/abs/2409.17928" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration</strong>. <br> Kaihang Pan, Zhaoyu Fan, Juncheng Li, Qifan Yu, Hao Fei, Siliang Tang, Richang Hong, Hanwang Zhang, Qianru Sun. [<a href="https://arxiv.org/abs/2409.19872" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Knowledge Editing with Dynamic Knowledge Graphs for Multi-hop Question Answering</strong>. <br> Yifan Lu, Yigeng Zhou, Jing Li, Yequan Wang, Xuebo Liu, Daojing He, Fangming Liu, Min Zhang. [<a href="https://arxiv.org/abs/2412.13782" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>Everything is Editable: Extend Knowledge Editing to Unstructured Data in Large Language Models</strong>. <br> Jingcheng Deng, Zihao Wei, Liang Pang, Hanxing Ding, Huawei Shen, Xueqi Cheng. [<a href="https://arxiv.org/abs/2405.15349" rel="nofollow">paper</a>]</p> </li> <li> <p dir="auto"><strong>SAKE: Steering Activations for Knowledge Editing</strong>. <br> Marco Scialanga, Thibault Laugel, Vincent Grari, Marcin Detyniecki. [<a href="https://arxiv.org/abs/2503.01751" rel="nofollow">paper</a>]</p> </li> </ol> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Analysis</h3><a id="user-content-analysis" class="anchor" aria-label="Permalink: Analysis" href="#analysis"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li><strong>Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models.</strong> <br> Peter Hase, Mohit Bansal, Been Kim, Asma Ghandeharioun. [<a href="https://arxiv.org/pdf/2301.04213.pdf" rel="nofollow">paper</a>] [<a href="https://github.com/google/belief-localization">code</a>]</li> <li><strong>Dissecting Recall of Factual Associations in Auto-Regressive Language Models</strong> <br> Mor Geva, Jasmijn Bastings, Katja Filippova, Amir Globerson. [<a href="https://arxiv.org/abs/2304.14767" rel="nofollow">paper</a>]</li> <li><strong>Evaluating the Ripple Effects of Knowledge Editing in Language Models</strong> <br> Roi Cohen, Eden Biran, Ori Yoran, Amir Globerson, Mor Geva. [<a href="https://arxiv.org/abs/2307.12976" rel="nofollow">paper</a>]</li> <li><strong>Edit at your own risk: evaluating the robustness of edited models to distribution shifts.</strong> <br> Davis Brown, Charles Godfrey, Cody Nizinski, Jonathan Tu, Henry Kvinge. [<a href="https://arxiv.org/abs/2303.00046" rel="nofollow">paper</a>]</li> <li><strong>Journey to the Center of the Knowledge Neurons: Discoveries of Language-Independent Knowledge Neurons and Degenerate Knowledge Neurons.</strong> (AAAI 2024) <br> Yuheng Chen, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao. [<a href="https://arxiv.org/abs/2308.13198" rel="nofollow">paper</a>]</li> <li><strong>Linearity of Relation Decoding in Transformer Language Models</strong><br> Evan Hernandez, Martin Wattenberg, Arnab Sen Sharma, Jacob Andreas, Tal Haklay, Yonatan Belinkov, Kevin Meng, David Bau. [<a href="https://arxiv.org/pdf/2308.09124.pdf" rel="nofollow">paper</a>]</li> <li><strong>KLoB: a Benchmark for Assessing Knowledge Locating Methods in Language Models</strong><br> Yiming Ju, Zheng Zhang. [<a href="http://export.arxiv.org/pdf/2309.16535" rel="nofollow">paper</a>]</li> <li><strong>Inference-Time Intervention: Eliciting Truthful Answers from a Language Model</strong> (NeurIPS 2023) <br> Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, Martin Wattenberg. [<a href="https://arxiv.org/abs/2306.03341" rel="nofollow">paper</a>] [<a href="https://github.com/likenneth/honest_llama">code</a>]</li> <li><strong>Emptying the Ocean with a Spoon: Should We Edit Models?</strong> (EMNLP 2023 Findings) <br> Yuval Pinter and Michael Elhadad. [<a href="https://arxiv.org/pdf/2310.11958.pdf" rel="nofollow">paper</a>]</li> <li><strong>Unveiling the Pitfalls of Knowledge Editing for Large Language Models</strong> <br> Zhoubo Li, Ningyu Zhang, Yunzhi Yao, Mengru Wang, Xi Chen and Huajun Chen. [<a href="https://arxiv.org/pdf/2310.02129.pdf" rel="nofollow">paper</a>]</li> <li><strong>Editing Personality for LLMs</strong> <br> Shengyu Mao, Ningyu Zhang, Xiaohan Wang, Mengru Wang, Yunzhi Yao, Yong Jiang, Pengjun Xie, Fei Huang and Huajun Chen. [<a href="https://arxiv.org/pdf/2310.02168.pdf" rel="nofollow">paper</a>]</li> <li><strong>Evaluating Dependencies in Fact Editing for Language Models: Specificity and Implication Awareness</strong>（Findings of EMNLP2023） <br> Zichao Li, Ines Arous, Siva Reddy, Jackie C.K. Cheung [<a href="https://arxiv.org/pdf/2312.01858.pdf" rel="nofollow">paper</a>]</li> <li><strong>Finding and Editing Multi-Modal Neurons in Pre-Trained Transformer</strong> <br> Haowen Pan,Yixin Cao,Xiaozhi Wang,Xun Yang. [<a href="https://arxiv.org/abs/2311.07470" rel="nofollow">paper</a>]</li> <li><strong>Assessing Knowledge Editing in Language Models via Relation Perspective</strong> <br> Yifan Wei,Xiaoyan Yu,Huanhuan Ma,Fangyu Lei,Yixuan Weng,Ran Song,Kang Liu. [<a href="https://arxiv.org/abs/2311.09053" rel="nofollow">paper</a>]</li> <li><strong>History Matters: Temporal Knowledge Editing in Large Language Model</strong>（AAAI 2024） <br> Xunjian Yin,Jin Jiang,Liming Yang,Xiaojun Wan. [<a href="https://arxiv.org/abs/2312.05497" rel="nofollow">paper</a>]</li> <li><strong>Cross-Lingual Knowledge Editing in Large Language Models</strong> <br> Jiaan Wang, Yunlong Liang, Zengkui Sun, Yuxuan Cao, Jiarong Xu. [<a href="https://arxiv.org/pdf/2309.08952.pdf" rel="nofollow">paper</a>]</li> <li><strong>Large Language Models Relearn Removed Concepts</strong> <br> Michelle Lo, Shay B. Cohen, Fazl Barez [<a href="https://arxiv.org/abs/2401.01814" rel="nofollow">paper</a>]</li> <li><strong>Model Editing Can Hurt General Abilities of Large Language Models</strong> <br> Jia-Chen Gu, Hao-Xiang Xu, Jun-Yu Ma, Pan Lu, Zhen-Hua Ling, Kai-Wei Chang, Nanyun Peng [<a href="https://arxiv.org/pdf/2401.04700.pdf" rel="nofollow">paper</a>]</li> <li><strong>Model Editing at Scale leads to Gradual and Catastrophic Forgetting</strong> <br> Akshat Gupta, Anurag Rao, Gopala Anumanchipalli. [<a href="https://arxiv.org/abs/2401.07453" rel="nofollow">paper</a>]</li> <li><strong>Propagation and Pitfalls: Reasoning-based Assessment of Knowledge Editing through Counterfactual Tasks</strong> <br> Wenyue Hua, Jiang Guo, Mingwen Dong, Henghui Zhu, Patrick Ng, Zhiguo Wang. [<a href="https://arxiv.org/abs/2401.17585" rel="nofollow">paper</a>]</li> <li><strong>Long-form evaluation of model editing</strong> <br> Domenic Rosati, Robie Gonzales, Jinkun Chen, Xuemin Yu. [<a href="https://arxiv.org/pdf/2402.09394.pdf" rel="nofollow">paper</a>]</li> <li><strong>The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse</strong> <br> Wanli Yang, Fei Sun, Xinyu Ma, Xun Liu, Dawei Yin, Xueqi Cheng. [<a href="https://arxiv.org/pdf/2402.09656.pdf" rel="nofollow">paper</a>]</li> <li><strong>The Da Vinci Code of Large Pre-trained Language Models: Deciphering Degenerate Knowledge Neurons</strong> <br> Yuheng Chen,Pengfei Cao,Yubo Chen,Yining Wang,Shengping Liu,Kang Liu,Jun Zhao. [<a href="https://arxiv.org/abs/2402.13731" rel="nofollow">paper</a>]</li> <li><strong>Navigating the Dual Facets: A Comprehensive Evaluation of Sequential Memory Editing in Large Language Models</strong> <br> Zihao Lin, Mohammad Beigi, Hongxuan Li, Yufan Zhou, Yuxiang Zhang, Qifan Wang, Wenpeng Yin, Lifu Huang. [<a href="https://www.arxiv.org/abs/2402.11122" rel="nofollow">paper</a>]</li> <li><strong>“Flex Tape Can’t Fix That”:Bias and Misinformation in Edited Language Models</strong> <br> Karina Halevy, Anna Sotnikova, Badr AlKhamissi, Syrielle Montariol, Antoine Bosselut. [<a href="https://arxiv.org/pdf/2403.00180" rel="nofollow">paper</a>]</li> <li><strong>The Missing Piece in Model Editing: A Deep Dive into the Hidden Damage Brought By Model Editing</strong> <br> Jianchen Wang,Zhouhong Gu,Zhuozhi Xiong,Hongwei Feng,Yanghua Xiao. [<a href="https://arxiv.org/abs/2403.07825" rel="nofollow">paper</a>]</li> <li><strong>Beyond Memorization: The Challenge of Random Memory Access in Language Models</strong> <br> Tongyao Zhu,Qian Liu,Liang Pang,Zhengbao Jiang,Min-Yen Kan,Min Lin. [<a href="https://arxiv.org/abs/2403.07805" rel="nofollow">paper</a>]</li> <li><strong>Interpreting Key Mechanisms of Factual Recall in Transformer-Based Language Models</strong> <br> Ang Lv, Kaiyi Zhang, Yuhan Chen, Yulong Wang, Lifeng Liu, Ji-Rong Wen, Jian Xie, Rui Yan. [<a href="https://arxiv.org/abs/2403.19521" rel="nofollow">paper</a>]</li> <li><strong>MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models</strong> <br> Zihao Wei,Jingcheng Deng,Liang Pang,Hanxing Ding,Huawei Shen,Xueqi Cheng. [<a href="https://arxiv.org/abs/2404.04990" rel="nofollow">paper</a>]</li> <li><strong>Is Your LLM Outdated? Benchmarking LLMs & Alignment Algorithms for Time-Sensitive Knowledge</strong> <br> Seyed Mahed Mousavi, Simone Alghisi, Giuseppe Riccardi. [<a href="https://arxiv.org/abs/2404.08700" rel="nofollow">paper</a>]</li> <li><strong>Neighboring Perturbations of Knowledge Editing on Large Language Models</strong>（ICML 2024）<br> Jun-Yu Ma, Jia-Chen Gu, Ningyu Zhang, Zhen-Hua Ling. [<a href="https://arxiv.org/pdf/2401.17623" rel="nofollow">paper</a>]</li> <li><strong>Event-level Knowledge Editing</strong><br> Hao Peng, Xiaozhi Wang, Chunyang Li, Kaisheng Zeng, Jiangshan Duo, Yixin Cao, Lei Hou, Juanzi Li. [<a href="https://arxiv.org/abs/2402.13093" rel="nofollow">paper</a>]</li> <li><strong>Updating Language Models with Unstructured Facts: Towards Practical Knowledge Editing</strong><br> Xiaobao Wu, Liangming Pan, William Yang Wang, Anh Tuan Luu. [<a href="https://arxiv.org/abs/2402.18909" rel="nofollow">paper</a>]</li> <li><strong>Detecting Edited Knowledge in Language Models</strong><br> Paul Youssef, Zhixue Zhao, Jörg Schlötterer, Christin Seifert. [<a href="https://arxiv.org/abs/2405.02765" rel="nofollow">paper</a>]</li> <li><strong>Perturbation-Restrained Sequential Model Editing</strong><br> Jun-Yu Ma, Hong Wang, Hao-Xiang Xu, Zhen-Hua Ling, Jia-Chen Gu. [<a href="https://arxiv.org/abs/2405.16821" rel="nofollow">paper</a>]</li> <li><strong>Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top</strong><br> Keyuan Cheng, Muhammad Asif Ali, Shu Yang, Gang Lin, Yuxuan Zhai, Haoyang Fei, Ke Xu, Lu Yu, Lijie Hu, Di Wang. [<a href="https://arxiv.org/abs/2405.15452" rel="nofollow">paper</a>]</li> <li><strong>Model Editing by Standard Fine-Tuning</strong><br> Govind Gangadhar, Karl Stratos. [<a href="https://arxiv.org/abs/2402.11078v3" rel="nofollow">paper</a>]</li> <li><strong>AI-native Memory: A Pathway from LLMs Towards AGI</strong><br> Jingbo Shang, Zai Zheng, Xiang Ying, Felix Tao, Mindverse Team. [<a href="https://arxiv.org/abs/2406.18312" rel="nofollow">paper</a>]</li> <li><strong>Intrinsic Evaluation of Unlearning Using Parametric Knowledge Traces</strong><br> Yihuai Hong, Lei Yu, Shauli Ravfogel, Haiqin Yang, Mor Geva. [<a href="https://arxiv.org/abs/2406.11614" rel="nofollow">paper</a>]</li> <li><strong>Can Editing LLMs Inject Harm?</strong><br> Canyu Chen, Baixiang Huang, Zekun Li, Zhaorun Chen, Shiyang Lai, Xiongxiao Xu, Jia-Chen Gu, Jindong Gu, Huaxiu Yao, Chaowei Xiao, Xifeng Yan, William Yang Wang, Philip Torr, Dawn Song, Kai Shu. [<a href="https://arxiv.org/abs/2407.20224" rel="nofollow">paper</a>]</li> <li><strong>Editing Conceptual Knowledge for Large Language Models</strong> (EMNLP 2024 Findings) <br> Xiaohan Wang, Shengyu Mao, Ningyu Zhang, Shumin Deng, Yunzhi Yao, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen. [<a href="https://arxiv.org/abs/2403.06259" rel="nofollow">paper</a>]</li> <li><strong>Knowledge Circuits in Pretrained Transformers</strong> (NeurIPS 2024) <br> Yunzhi Yao, Ningyu Zhang, Zekun Xi, Mengru Wang, Ziwen Xu, Shumin Deng, Huajun Chen. [<a href="https://arxiv.org/abs/2405.17969" rel="nofollow">paper</a>]</li> <li><strong>"Why" Has the Least Side Effect on Model Editing</strong> <br> Tsung-Hsuan Pan, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen. [<a href="https://arxiv.org/abs/2409.18679" rel="nofollow">paper</a>]</li> <li><strong>Cross-Lingual Multi-Hop Knowledge Editing</strong>. <br> Aditi Khandelwal, Harman Singh, Hengrui Gu, Tianlong Chen, Kaixiong Zhou. [<a href="https://arxiv.org/abs/2407.10275" rel="nofollow">paper</a>]</li> <li><strong>Can We Reverse In-Context Knowledge Edits?</strong><br> Paul Youssef, Zhixue Zhao, Jörg Schlötterer, Christin Seifert. [<a href="https://arxiv.org/abs/2410.12586" rel="nofollow">paper</a>]</li> <li><strong>Model Editing for LLMs4Code: How Far are We?</strong><br> Xiaopeng Li, Shangwen Wang, Shasha Li, Jun Ma, Jie Yu, Xiaodong Liu, Jing Wang, Bin Ji, Weimin Zhang. [<a href="https://arxiv.org/abs/2411.06638" rel="nofollow">paper</a>]</li> <li><strong>Backward Lens: Projecting Language Model Gradients into the Vocabulary Space</strong><br> Shahar Katz, Yonatan Belinkov, Mor Geva, Lior Wolf. [<a href="https://arxiv.org/abs/2402.12865" rel="nofollow">paper</a>]</li> <li><strong>Position: Editing Large Language Models Poses Serious Safety Risks</strong><br> Paul Youssef, Zhixue Zhao, Daniel Braun, Jörg Schlötterer, Christin Seifert. [<a href="https://arxiv.org/abs/2502.02958" rel="nofollow">paper</a>]</li> <li><strong>The Mirage of Model Editing: Revisiting Evaluation in the Wild</strong><br> Wanli Yang, Fei Sun, Jiajun Tan, Xinyu Ma, Qi Cao, Dawei Yin, Huawei Shen, Xueqi Cheng. [<a href="https://arxiv.org/abs/2502.11177" rel="nofollow">paper</a>]</li> <li><strong>Position: Editing Large Language Models Poses Serious Safety Risks</strong><br> Paul Youssef, Zhixue Zhao, Daniel Braun, Jörg Schlötterer, Christin Seifert. [<a href="https://arxiv.org/abs/2502.02958" rel="nofollow">paper</a>]</li> <li><strong>Constraining Sequential Model Editing with Editing Anchor Compression</strong>. <br> Hao-Xiang Xu, Jun-Yu Ma, Zhen-Hua Ling, Ningyu Zhang, Jia-Chen Gu. [<a href="https://arxiv.org/abs/2503.00035" rel="nofollow">paper</a>]</li> </ol> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">🧰 Resources</h2><a id="user-content--resources-1" class="anchor" aria-label="Permalink: 🧰 Resources" href="#-resources-1"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Benchmarks and Tasks</h3><a id="user-content-benchmarks-and-tasks" class="anchor" aria-label="Permalink: Benchmarks and Tasks" href="#benchmarks-and-tasks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th align="center">Edit Type</th> <th>Benchmarks & Datasets</th> </tr> </thead> <tbody> <tr> <td align="center"><strong>Fact Knowledge</strong></td> <td><a href="https://github.com/nicola-decao/KnowledgeEditor">ZSRE</a>, <a href="https://arxiv.org/abs/2305.13172" rel="nofollow">ZSRE plus</a>, <a href="https://rome.baulab.info/" rel="nofollow">CounterFact</a>,<a href="https://arxiv.org/abs/2305.13172" rel="nofollow">CounterFact plus</a>, <a href="https://arxiv.org/abs/2305.17553" rel="nofollow">CounterFact+</a>,<a href="https://github.com/yasumasaonoe/entity_knowledge_propagation">ECBD</a>, <a href="https://github.com/yasumasaonoe/entity_knowledge_propagation">MQUAKE</a>,<a href="https://arxiv.org/abs/2312.01858" rel="nofollow">DepEdit</a></td> </tr> <tr> <td align="center"><strong>Multi-Lingual</strong></td> <td><a href="https://arxiv.org/pdf/2309.08952.pdf" rel="nofollow">Bi-ZsRE</a>,<a href="https://arxiv.org/pdf/2308.09954.pdf" rel="nofollow">Eva-KELLM</a>, <a href="https://arxiv.org/abs/2312.13040" rel="nofollow">MzsRE</a>,<a href="https://www.arxiv.org/abs/2407.10275" rel="nofollow">CROLIN-MQUAKE</a></td> </tr> <tr> <td align="center"><strong>Sentiment</strong></td> <td><a href="https://arxiv.org/abs/2206.06520" rel="nofollow">Convsent</a></td> </tr> <tr> <td align="center"><strong>Bias</strong></td> <td><a href="https://arxiv.org/pdf/2304.00740.pdf" rel="nofollow">Bias in Bios</a></td> </tr> <tr> <td align="center"><strong>Hallucination</strong></td> <td><a href="https://github.com/Thartvigsen/GRACE/tree/main">WikiBio</a></td> </tr> <tr> <td align="center"><strong>Commonsense</strong></td> <td><a href="https://arxiv.org/abs/2305.14956" rel="nofollow">MEMIT<sub>csk</sub></a></td> </tr> <tr> <td align="center"><strong>concept</strong></td> <td><a href="https://arxiv.org/abs/2403.06259" rel="nofollow">ConceptEdit</a>, <a href="https://arxiv.org/abs/2406.11614" rel="nofollow">CONCEPTVECTORS</a></td> </tr> <tr> <td align="center"><strong>Reasoning</strong></td> <td><a href="https://arxiv.org/pdf/2308.09954.pdf" rel="nofollow">Eva-KELLM</a></td> </tr> <tr> <td align="center"><strong>Privacy Infomation Protect</strong></td> <td><a href="https://arxiv.org/abs/2310.02224" rel="nofollow">PrivQA</a>, <a href="https://arxiv.org/pdf/2309.11852.pdf" rel="nofollow">Knowledge Sanitation</a>,<a href="https://arxiv.org/pdf/2310.20138.pdf" rel="nofollow">Enron</a></td> </tr> <tr> <td align="center"><strong>Unified Benchmark</strong></td> <td><a href="https://arxiv.org/pdf/2311.16087.pdf" rel="nofollow">DUnE</a></td> </tr> <tr> <td align="center"><strong>Toxic Information</strong></td> <td><a href="https://arxiv.org/abs/2009.11462" rel="nofollow">RealToxicityPrompts</a>,<a href="https://arxiv.org/pdf/2308.08090.pdf" rel="nofollow">Toxicity Unlearning</a></td> </tr> <tr> <td align="center"><strong>MultiModal</strong></td> <td><a href="https://arxiv.org/abs/2310.08475" rel="nofollow">MMEdit</a> <a href="https://arxiv.org/abs/2403.07350" rel="nofollow">VLKEB</a>, <a href="https://arxiv.org/abs/2406.13219" rel="nofollow">MC-MKE</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Tools</h3><a id="user-content-tools" class="anchor" aria-label="Permalink: Tools" href="#tools"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://github.com/zjunlp/EasyEdit">EasyEdit</a>: An Easy-to-use Knowledge Editing Framework for Large Language Models.</p> <p dir="auto"><a href="https://github.com/hiyouga/FastEdit">FastEdit</a>: Editing large language models within 10 seconds</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citation</h2><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Please cite our paper if find our work useful.</p> <div class="highlight highlight-text-bibtex notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=" @article{zhang2024comprehensive, title={A Comprehensive Study of Knowledge Editing for Large Language Models}, author={Zhang, Ningyu and Yao, Yunzhi and Tian, Bozhong and Wang, Peng and Deng, Shumin and Wang, Mengru and Xi, Zekun and Mao, Shengyu and Zhang, Jintian and Ni, Yuansheng and others}, journal={arXiv preprint arXiv:2401.01286}, year={2024} } @article{DBLP:journals/corr/abs-2305-13172, author = {Yunzhi Yao and Peng Wang and Bozhong Tian and Siyuan Cheng and Zhoubo Li and Shumin Deng and Huajun Chen and Ningyu Zhang}, title = {Editing Large Language Models: Problems, Methods, and Opportunities}, journal = {CoRR}, volume = {abs/2305.13172}, year = {2023}, url = {https://doi.org/10.48550/arXiv.2305.13172}, doi = {10.48550/arXiv.2305.13172}, eprinttype = {arXiv}, eprint = {2305.13172}, timestamp = {Tue, 30 May 2023 17:04:46 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2305-13172.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } @inproceedings{wang2024knowledge, title={Knowledge Mechanisms in Large Language Models: A Survey and Perspective}, author={Wang, Mengru and Yao, Yunzhi and Xu, Ziwen and Qiao, Shuofei and Deng, Shumin and Wang, Peng and Chen, Xiang and Gu, Jia-Chen and Jiang, Yong and Xie, Pengjun and others}, booktitle={Findings of the Association for Computational Linguistics: EMNLP 2024}, pages={7097--7135}, year={2024} }"><pre><span class="pl-k">@article</span>{<span class="pl-en">zhang2024comprehensive</span>, <span class="pl-s">title</span>=<span class="pl-s"><span class="pl-pds">{</span>A Comprehensive Study of Knowledge Editing for Large Language Models<span class="pl-pds">}</span></span>, <span class="pl-s">author</span>=<span class="pl-s"><span class="pl-pds">{</span>Zhang, Ningyu and Yao, Yunzhi and Tian, Bozhong and Wang, Peng and Deng, Shumin and Wang, Mengru and Xi, Zekun and Mao, Shengyu and Zhang, Jintian and Ni, Yuansheng and others<span class="pl-pds">}</span></span>, <span class="pl-s">journal</span>=<span class="pl-s"><span class="pl-pds">{</span>arXiv preprint arXiv:2401.01286<span class="pl-pds">}</span></span>, <span class="pl-s">year</span>=<span class="pl-s"><span class="pl-pds">{</span>2024<span class="pl-pds">}</span></span> } <span class="pl-k">@article</span>{<span class="pl-en">DBLP:journals/corr/abs-2305-13172</span>, <span class="pl-s">author</span> = <span class="pl-s"><span class="pl-pds">{</span>Yunzhi Yao and</span> <span class="pl-s"> Peng Wang and</span> <span class="pl-s"> Bozhong Tian and</span> <span class="pl-s"> Siyuan Cheng and</span> <span class="pl-s"> Zhoubo Li and</span> <span class="pl-s"> Shumin Deng and</span> <span class="pl-s"> Huajun Chen and</span> <span class="pl-s"> Ningyu Zhang<span class="pl-pds">}</span></span>, <span class="pl-s">title</span> = <span class="pl-s"><span class="pl-pds">{</span>Editing Large Language Models: Problems, Methods, and Opportunities<span class="pl-pds">}</span></span>, <span class="pl-s">journal</span> = <span class="pl-s"><span class="pl-pds">{</span>CoRR<span class="pl-pds">}</span></span>, <span class="pl-s">volume</span> = <span class="pl-s"><span class="pl-pds">{</span>abs/2305.13172<span class="pl-pds">}</span></span>, <span class="pl-s">year</span> = <span class="pl-s"><span class="pl-pds">{</span>2023<span class="pl-pds">}</span></span>, <span class="pl-s">url</span> = <span class="pl-s"><span class="pl-pds">{</span>https://doi.org/10.48550/arXiv.2305.13172<span class="pl-pds">}</span></span>, <span class="pl-s">doi</span> = <span class="pl-s"><span class="pl-pds">{</span>10.48550/arXiv.2305.13172<span class="pl-pds">}</span></span>, <span class="pl-s">eprinttype</span> = <span class="pl-s"><span class="pl-pds">{</span>arXiv<span class="pl-pds">}</span></span>, <span class="pl-s">eprint</span> = <span class="pl-s"><span class="pl-pds">{</span>2305.13172<span class="pl-pds">}</span></span>, <span class="pl-s">timestamp</span> = <span class="pl-s"><span class="pl-pds">{</span>Tue, 30 May 2023 17:04:46 +0200<span class="pl-pds">}</span></span>, <span class="pl-s">biburl</span> = <span class="pl-s"><span class="pl-pds">{</span>https://dblp.org/rec/journals/corr/abs-2305-13172.bib<span class="pl-pds">}</span></span>, <span class="pl-s">bibsource</span> = <span class="pl-s"><span class="pl-pds">{</span>dblp computer science bibliography, https://dblp.org<span class="pl-pds">}</span></span> } <span class="pl-k">@inproceedings</span>{<span class="pl-en">wang2024knowledge</span>, <span class="pl-s">title</span>=<span class="pl-s"><span class="pl-pds">{</span>Knowledge Mechanisms in Large Language Models: A Survey and Perspective<span class="pl-pds">}</span></span>, <span class="pl-s">author</span>=<span class="pl-s"><span class="pl-pds">{</span>Wang, Mengru and Yao, Yunzhi and Xu, Ziwen and Qiao, Shuofei and Deng, Shumin and Wang, Peng and Chen, Xiang and Gu, Jia-Chen and Jiang, Yong and Xie, Pengjun and others<span class="pl-pds">}</span></span>, <span class="pl-s">booktitle</span>=<span class="pl-s"><span class="pl-pds">{</span>Findings of the Association for Computational Linguistics: EMNLP 2024<span class="pl-pds">}</span></span>, <span class="pl-s">pages</span>=<span class="pl-s"><span class="pl-pds">{</span>7097--7135<span class="pl-pds">}</span></span>, <span class="pl-s">year</span>=<span class="pl-s"><span class="pl-pds">{</span>2024<span class="pl-pds">}</span></span> }</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">🎉Contribution</h2><a id="user-content-contribution" class="anchor" 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style="max-width: 100%;"> </a> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Contributing to this paper list</h3><a id="user-content-contributing-to-this-paper-list" class="anchor" aria-label="Permalink: Contributing to this paper list" href="#contributing-to-this-paper-list"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>There are cases where we miss important works in this field, please contribute to this repo! Thanks for the efforts in advance.</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Acknowledgement</h3><a id="user-content-acknowledgement" class="anchor" aria-label="Permalink: Acknowledgement" href="#acknowledgement"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>We would like to express our gratitude to <a href="https://yulonghui.github.io/" rel="nofollow">Longhui Yu</a> for the kind reminder about the missing papers.</li> </ul> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="KgduKziIYl/A5Hqf+Yo0cnXR+XLYcqRK2Vddj8Gy1a4lqFyEHsr2hA69yKqHjehV0vQSRPkC1fjHC9R5RhFoLQ==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> Must-read Papers on Knowledge Editing for Large Language Models. </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/review" title="Topic: review" data-view-component="true" class="topic-tag topic-tag-link"> review </a> <a href="/topics/natural-language-processing" title="Topic: natural-language-processing" data-view-component="true" class="topic-tag topic-tag-link"> natural-language-processing </a> <a href="/topics/paper" title="Topic: paper" data-view-component="true" class="topic-tag topic-tag-link"> paper </a> <a href="/topics/survey" title="Topic: survey" data-view-component="true" class="topic-tag topic-tag-link"> survey </a> <a href="/topics/rome" title="Topic: rome" data-view-component="true" class="topic-tag topic-tag-link"> rome </a> <a href="/topics/paper-list" title="Topic: paper-list" data-view-component="true" class="topic-tag topic-tag-link"> paper-list </a> <a href="/topics/awsome-list" title="Topic: awsome-list" data-view-component="true" class="topic-tag topic-tag-link"> awsome-list </a> <a href="/topics/pre-trained-model" title="Topic: pre-trained-model" data-view-component="true" class="topic-tag topic-tag-link"> pre-trained-model </a> <a href="/topics/pre-trained-language-models" title="Topic: pre-trained-language-models" data-view-component="true" class="topic-tag topic-tag-link"> pre-trained-language-models </a> <a href="/topics/foundation-models" title="Topic: foundation-models" data-view-component="true" class="topic-tag topic-tag-link"> foundation-models </a> <a href="/topics/large-language-models" title="Topic: large-language-models" data-view-component="true" class="topic-tag topic-tag-link"> large-language-models </a> <a href="/topics/model-editing" title="Topic: model-editing" data-view-component="true" class="topic-tag topic-tag-link"> model-editing </a> <a href="/topics/knowledge-editing" title="Topic: knowledge-editing" data-view-component="true" class="topic-tag topic-tag-link"> knowledge-editing </a> <a href="/topics/knowlm" title="Topic: knowlm" data-view-component="true" class="topic-tag topic-tag-link"> knowlm </a> <a href="/topics/easyedit" title="Topic: easyedit" data-view-component="true" class="topic-tag topic-tag-link"> easyedit </a> 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