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GitHub - zjukg/KG-LLM-Papers: [Paper List] Papers integrating knowledge graphs (KGs) and large language models (LLMs)
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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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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" 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dir=\"auto\"\u003eKG-LLM-Papers\u003c/h1\u003e\u003ca id=\"user-content-kg-llm-papers\" class=\"anchor\" aria-label=\"Permalink: KG-LLM-Papers\" href=\"#kg-llm-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 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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\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eWhat can LLMs do for KGs? Or, in other words, what role can KG play in the era of LLMs?\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003e🙌 This repository collects papers integrating \u003cstrong\u003eknowledge graphs (KGs)\u003c/strong\u003e and \u003cstrong\u003elarge language models (LLMs)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e😎 Welcome to recommend missing papers through \u003cstrong\u003e\u003ccode\u003ePull Requests\u003c/code\u003e\u003c/strong\u003e.\u003c/p\u003e\n\n\u003cdetails\u003e\n \u003csummary\u003e👈 🔔 News \u003c/summary\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ccode\u003e2025-02\u003c/code\u003e We preprint our Paper \u003ca href=\"https://arxiv.org/abs/2502.05478\" rel=\"nofollow\"\u003eOntoTune: Ontology-Driven Self-training for Aligning Large Language Models\u003c/a\u003e (WWW 2025) [\u003ca href=\"https://github.com/zjukg/OntoTune\"\u003e\u003ccode\u003eRepo\u003c/code\u003e\u003c/a\u003e].\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ccode\u003e2025-02\u003c/code\u003e We preprint our Paper \u003ca href=\"https://arxiv.org/abs/2502.06257\" rel=\"nofollow\"\u003eK-ON: Stacking Knowledge On the Head Layer of Large Language Model\u003c/a\u003e (AAAI 2025 Oral) [\u003ca href=\"https://github.com/zjukg/K-ON\"\u003e\u003ccode\u003eRepo\u003c/code\u003e\u003c/a\u003e].\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ccode\u003e2025-01\u003c/code\u003e We preprint our Paper \u003ca href=\"https://arxiv.org/abs/2501.00244\" rel=\"nofollow\"\u003eHave We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking\u003c/a\u003e [\u003ca href=\"https://github.com/zjukg/SUBARU\"\u003e\u003ccode\u003eRepo\u003c/code\u003e\u003c/a\u003e].\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ccode\u003e2024-12\u003c/code\u003e We preprint our Paper \u003ca href=\"https://arxiv.org/abs/2406.18916\" rel=\"nofollow\"\u003eTrustUQA: A Trustful Framework for Unified Structured Data Question Answering\u003c/a\u003e (AAAI 2025) [\u003ca href=\"https://github.com/zjukg/TrustUQA\"\u003e\u003ccode\u003eRepo\u003c/code\u003e\u003c/a\u003e].\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ccode\u003e2024-09\u003c/code\u003e Our paper \u003ca href=\"https://openreview.net/forum?id=eqMNwXvOqn\" rel=\"nofollow\"\u003eMKGL: Mastery of a Three-Word Language\u003c/a\u003e has been accepted by NeurIPS 2024 as a spotlight paper. [\u003ca href=\"https://github.com/zjukg/MKGL\"\u003e\u003ccode\u003eRepo\u003c/code\u003e\u003c/a\u003e]\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ccode\u003e2024-07\u003c/code\u003e Our paper \u003ca href=\"https://arxiv.org/abs/2310.06671\" rel=\"nofollow\"\u003eMaking Large Language Models Perform Better in Knowledge Graph Completion\u003c/a\u003e has been accepted by ACM MM 2024 as an oral paper. [\u003ca href=\"https://github.com/zjukg/KoPA\"\u003e\u003ccode\u003eRepo\u003c/code\u003e\u003c/a\u003e]\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ccode\u003e2024-05\u003c/code\u003e Our paper \u003ca href=\"https://arxiv.org/abs/2311.06503\" rel=\"nofollow\"\u003eKnowledgeable Preference Alignment for LLMs in Domain-specific Question Answering\u003c/a\u003e has been accepted by ACL 2024. [\u003ca href=\"https://github.com/zjukg/KnowPAT\"\u003e\u003ccode\u003eRepo\u003c/code\u003e\u003c/a\u003e]\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ccode\u003e2024-02\u003c/code\u003e We preprint our Survey \u003ca href=\"http://arxiv.org/abs/2402.05391\" rel=\"nofollow\"\u003eKnowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey\u003c/a\u003e [\u003ca href=\"https://github.com/zjukg/KG-MM-Survey\"\u003e\u003ccode\u003eRepo\u003c/code\u003e\u003c/a\u003e].\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ccode\u003e2023-06\u003c/code\u003e We create this repository to maintain a paper list on \u003ccode\u003eIntergrating Knowledge Graphs and Large Language Models\u003c/code\u003e.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/details\u003e\n\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContent\u003c/h2\u003e\u003ca id=\"user-content-content\" class=\"anchor\" aria-label=\"Permalink: Content\" href=\"#content\"\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=\"#papers\"\u003e📜 Papers\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#surveys\"\u003e🔖 Surveys\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#methods\"\u003e⚙ Methods\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#resources-and-benchmarking\"\u003e🧰 Resources\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\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\"\u003ePapers\u003c/h2\u003e\u003ca id=\"user-content-papers\" class=\"anchor\" aria-label=\"Permalink: Papers\" href=\"#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\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\u003cul dir=\"auto\"\u003e\n\u003cli\u003e[\u003ca href=\"https://www.sciencedirect.com/science/article/pii/S1570826824000301\" rel=\"nofollow\"\u003eJoWS\u003c/a\u003e] Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective \u003ccode\u003e2024.12\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.05391\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey. \u003ccode\u003e2024.02\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2311.07914\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey. \u003ccode\u003e2023.11\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.07521\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity. \u003ccode\u003e2023.10\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.04835\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] On the Evolution of Knowledge Graphs: A Survey and Perspective. \u003ccode\u003e2023.10\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/pdf/2309.17122\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle? \u003ccode\u003e2023.09\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2309.01029\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Explainability for Large Language Models: A Survey. \u003ccode\u003e2023.09\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2308.14217\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact. \u003ccode\u003e2023.08\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2308.06374\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Large Language Models and Knowledge Graphs: Opportunities and Challenges. \u003ccode\u003e2023.08\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/pdf/2306.08302\" rel=\"nofollow\"\u003eTKDE\u003c/a\u003e] Unifying Large Language Models and Knowledge Graphs: A Roadmap. \u003ccode\u003e2023.06\u003c/code\u003e [\u003ca href=\"https://github.com/RManLuo/Awesome-LLM-KG\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/pdf/2306.11489.pdf\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] ChatGPT is not Enough: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling. \u003ccode\u003e2023.06\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2211.05994\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] A Survey of Knowledge-Enhanced Pre-trained Language Models. \u003ccode\u003e2023.05\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMethod\u003c/h3\u003e\u003ca id=\"user-content-method\" class=\"anchor\" aria-label=\"Permalink: Method\" href=\"#method\"\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=\"https://arxiv.org/abs/2502.10453\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Linking Cryptoasset Attribution Tags to Knowledge Graph Entities: An LLM-based Approach. \u003ccode\u003e2025.2\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2502.05478\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] OntoTune: Ontology-Driven Self-training for Aligning Large Language Models. \u003ccode\u003e2025.2\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://aclanthology.org/2024.findings-emnlp.524/\" rel=\"nofollow\"\u003eEMNLP 2024 findings\u003c/a\u003e] Question-guided Knowledge Graph Re-scoring and Injection for Knowledge Graph Question Answering. \u003ccode\u003e2024.11\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2407.11417\" rel=\"nofollow\"\u003eEMNLP 2024 findings\u003c/a\u003e] SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions. \u003ccode\u003e2024.11\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/pdf/2410.18415\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains. \u003ccode\u003e2024.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2410.12609\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Towards Graph Foundation Models: The Perspective of Zero-shot Reasoning on Knowledge Graphs. \u003ccode\u003e2024.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2410.07526\" rel=\"nofollow\"\u003eNeurIPS 2024\u003c/a\u003e] MKGL: Mastery of a Three-Word Language. \u003ccode\u003e2024.10\u003c/code\u003e [\u003ca href=\"https://github.com/zjukg/MKGL\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.06861\" rel=\"nofollow\"\u003eNeurIPS 2024\u003c/a\u003e] UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Construction. \u003ccode\u003e2024.10\u003c/code\u003e [\u003ca href=\"https://github.com/usail-hkust/UrbanKGent\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.16412\" rel=\"nofollow\"\u003eNeurIPS 2024\u003c/a\u003e] KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge. \u003ccode\u003e2024.10\u003c/code\u003e [\u003ca href=\"https://github.com/pat-jj/KG-FIT\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://openreview.net/forum?id=JCG0KTPVYy\" rel=\"nofollow\"\u003eICML 2024\u003c/a\u003e] Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models. \u003ccode\u003e2024.10\u003c/code\u003e [\u003ca href=\"https://github.com/shiliu-egg/ICML2024_COFT\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2410.02811\" rel=\"nofollow\"\u003eACL 2024\u003c/a\u003e] SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graphs. \u003ccode\u003e2024.09\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.16806\" rel=\"nofollow\"\u003eNeurIPS 2024\u003c/a\u003e] LLM4EA: Entity Alignment with Noisy Annotations from Large Language Models. \u003ccode\u003e2024.09\u003c/code\u003e [\u003ca href=\"https://github.com/chensyCN/llm4ea_official\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2407.00653\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language Models by Learning from Knowledge Graphs. \u003ccode\u003e2024.07\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2407.10793\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] GraphEval: A Knowledge-Graph Based LLM Hallucination Evaluation Framework. \u003ccode\u003e2024.07\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2407.10805\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Think-on-Graph 2.0: Deep and Interpretable Large Language Model Reasoning with Knowledge Graph-guided Retrieval. \u003ccode\u003e2024.07\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2407.16127\" rel=\"nofollow\"\u003eISWC 2024\u003c/a\u003e] Finetuning Generative Large Language Models with Discrimination Instructions for Knowledge Graph Completion. \u003ccode\u003e2024.07\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.16568\" rel=\"nofollow\"\u003eACL 2024 findings\u003c/a\u003e] Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models. \u003ccode\u003e2024.07\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2407.21358\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs. \u003ccode\u003e2024.07\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.07793\" rel=\"nofollow\"\u003eNAACL 2024 findings\u003c/a\u003e] GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models. \u003ccode\u003e2024.06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.11199\" rel=\"nofollow\"\u003eACL 2024 findings\u003c/a\u003e] Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models. \u003ccode\u003e2024.06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2406.03746\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Efficient Knowledge Infusion via KG-LLM Alignment. \u003ccode\u003e2024.06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2406.18114\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Knowledge Graph Enhanced Retrieval-Augmented Generation for Failure Mode and Effects Analysis. \u003ccode\u003e2024.06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2406.13862\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Knowledge Graph-Enhanced Large Language Models via Path Selection. \u003ccode\u003e2024.06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2406.14282\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs. \u003ccode\u003e2024.06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2406.02962\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Docs2KG: Unified Knowledge Graph Construction from Heterogeneous Documents Assisted by Large Language Models. \u003ccode\u003e2024.06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2406.02110\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] UniOQA: A Unified Framework for Knowledge Graph Question Answering with Large Language Model. \u003ccode\u003e2024.06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2406.02030\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Multimodal Reasoning with Multimodal Knowledge Graph. \u003ccode\u003e2024.06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2406.01391\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Knowledge Graph in Astronomical Research with Large Language Models: Quantifying Driving Forces in Interdisciplinary Scientific Discovery. \u003ccode\u003e2024.06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2406.01238\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] EffiQA: Efficient Question-Answering with Strategic Multi-Model Collaboration on Knowledge Graphs. \u003ccode\u003e2024.06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2406.01145\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Explore then Determine: A GNN-LLM Synergy Framework for Reasoning over Knowledge Graph. \u003ccode\u003e2024.06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2406.00036\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] EMERGE: Integrating RAG for Improved Multimodal EHR Predictive Modeling. \u003ccode\u003e2024.06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-024-00481-2\" rel=\"nofollow\"\u003eEPJ Data Science\u003c/a\u003e] Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models. \u003ccode\u003e2024.06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.20455\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] DepsRAG: Towards Managing Software Dependencies using Large Language Models. \u003ccode\u003e2024.06\u003c/code\u003e [\u003ca href=\"https://github.com/Mohannadcse/DepsRAG\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.19877\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] KNOW: A Real-World Ontology for Knowledge Capture with Large Language Models \u003ccode\u003e2024.05\u003c/code\u003e [\u003ca href=\"https://github.com/KnowOntology\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.14831\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models \u003ccode\u003e2024.05\u003c/code\u003e [\u003ca href=\"https://github.com/OSU-NLP-Group/HippoRAG\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.14012\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Prompt-Time Ontology-Driven Symbolic Knowledge Capture with Large Language Models \u003ccode\u003e2024.05\u003c/code\u003e [\u003ca href=\"https://github.com/HaltiaAI/paper-PTODSKC\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.10288\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Timeline-based Sentence Decomposition with In-Context Learning for Temporal Fact Extraction. \u003ccode\u003e2024.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.09713\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] SOK-Bench: A Situated Video Reasoning Benchmark with Aligned Open-World Knowledge. \u003ccode\u003e2024.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.06545\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrieval. \u003ccode\u003e2024.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.06524\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts. \u003ccode\u003e2024.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.04819\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature. \u003ccode\u003e2024.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.04756\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] BiasKG: Adversarial Knowledge Graphs to Induce Bias in Large Language Models. \u003ccode\u003e2024.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.04753\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] AttacKG+:Boosting Attack Knowledge Graph Construction with Large Language Models. \u003ccode\u003e2024.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.04180\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Sora Detector: A Unified Hallucination Detection for Large Text-to-Video Models. \u003ccode\u003e2024.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.03734\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] FOKE: A Personalized and Explainable Education Framework Integrating Foundation Models, Knowledge Graphs, and Prompt Engineering. \u003ccode\u003e2024.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.02738\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Relations Prediction for Knowledge Graph Completion using Large Language Models. \u003ccode\u003e2024.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.02105\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Evaluating Large Language Models for Structured Science Summarization in the Open Research Knowledge Graph. \u003ccode\u003e2024.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.01649\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning. \u003ccode\u003e2024.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2405.00449\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models. \u003ccode\u003e2024.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.19744\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] PrivComp-KG : Leveraging Knowledge Graph and Large Language Models for Privacy Policy Compliance Verification. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.19234\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Multi-hop Question Answering over Knowledge Graphs using Large Language Models. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.19146\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Automated Construction of Theme-specific Knowledge Graphs. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.17723\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.17000\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Evaluating Class Membership Relations in Knowledge Graphs using Large Language Models. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.15923\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] KGValidator: A Framework for Automatic Validation of Knowledge Graph Construction. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.13865\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Context-Enhanced Language Models for Generating Multi-Paper Citations. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.10384\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Reasoning on Efficient Knowledge Paths:Knowledge Graph Guides Large Language Model for Domain Question Answering. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.09763\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] KG-CTG: Citation Generation through Knowledge Graph-guided Large Language Models. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.09077\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] CuriousLLM: Elevating Multi-Document QA with Reasoning-Infused Knowledge Graph Prompting. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.07677\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] ODA: Observation-Driven Agent for integrating LLMs and Knowledge Graphs. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.06571\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Building A Knowledge Graph to Enrich ChatGPT Responses in Manufacturing Service Discovery. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.04264\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.03868\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://openreview.net/forum?id=dWYRjT501w\" rel=\"nofollow\"\u003eCOLM 2024\u003c/a\u003e] Unveiling LLMs: The Evolution of Latent Representations in a Dynamic Knowledge Graph. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.03080\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Construction of Functional Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.02389\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] On Linearizing Structured Data in Encoder-Decoder Language Models: Insights from Text-to-SQL. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.01720\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Self-Improvement Programming for Temporal Knowledge Graph Question Answering. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.01425\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] A Preliminary Roadmap for LLMs as Assistants in Exploring, Analyzing, and Visualizing Knowledge Graphs. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.00942\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.00589\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.00209\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.14741\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2404.16130\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] From Local to Global: A Graph RAG Approach to Query-Focused Summarization. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2312.15883\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses. \u003ccode\u003e2024.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.14950\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.17532\" rel=\"nofollow\"\u003eLREC-COLING 2024\u003c/a\u003e] KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.14253\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] K-Act2Emo: Korean Commonsense Knowledge Graph for Indirect Emotional Expression. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.12151\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Fusing Domain-Specific Content from Large Language Models into Knowledge Graphs for Enhanced Zero Shot Object State Classification. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.11786\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Construction of Hyper-Relational Knowledge Graphs Using Pre-Trained Large Language Models. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.08593\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.08345\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] From human experts to machines: An LLM supported approach to ontology and knowledge graph construction. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.07398\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Complex Reasoning over Logical Queries on Commonsense Knowledge Graphs. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.07311\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Knowledge Graph Large Language Model (KG-LLM) for Link Prediction. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.05881\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.04261\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Advancing Biomedical Text Mining with Community Challenges. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.03008\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Knowledge Graphs as Context Sources for LLM-Based Explanations of Learning Recommendations. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.02966\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.02576\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] AceMap: Knowledge Discovery through Academic Graph. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.02253\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] KnowPhish: Large Language Models Meet Multimodal Knowledge Graphs for Enhancing Reference-Based Phishing Detection. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.02014\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Unveiling Hidden Links Between Unseen Security Entities. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.01972\" rel=\"nofollow\"\u003eLREC-COLING 2024\u003c/a\u003e] Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.01481\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Infusing Knowledge into Large Language Models with Contextual Prompts. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.01395\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] CR-LT-KGQA: A Knowledge Graph Question Answering Dataset Requiring Commonsense Reasoning and Long-Tail Knowledge. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.01390\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Right for Right Reasons: Large Language Models for Verifiable Commonsense Knowledge Graph Question Answering. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.01382\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Automatic Question-Answer Generation for Long-Tail Knowledge. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2403.00953\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] AutoRD: An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontologies-enhanced Large Language Models. \u003ccode\u003e2024.03\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.17786\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Stepwise Self-Consistent Mathematical Reasoning with Large Language Models. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.16568\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.15048\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Unlocking the Power of Large Language Models for Entity Alignment. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.14382\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Enhancing Temporal Knowledge Graph Forecasting with Large Language Models via Chain-of-History Reasoning. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.13750\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.13593\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Knowledge Graph Enhanced Large Language Model Editing. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.12728\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Modality-Aware Integration with Large Language Models for Knowledge-based Visual Question Answering. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.12352\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Graph-Based Retriever Captures the Long Tail of Biomedical Knowledge. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.11804\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] LLM as Prompter: Low-resource Inductive Reasoning on Arbitrary Knowledge Graphs. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.11541\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Counter-intuitive: Large Language Models Can Better Understand Knowledge Graphs Than We Thought. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.11441\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.11323\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Towards Development of Automated Knowledge Maps and Databases for Materials Engineering using Large Language Models. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.11163\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.11034\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] PAT-Questions: A Self-Updating Benchmark for Present-Anchored Temporal Question-Answering. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.10779\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] A Condensed Transition Graph Framework for Zero-shot Link Prediction with Large Language Models. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.09911\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Enhancing Large Language Models with Pseudo- and Multisource- Knowledge Graphs for Open-ended Question Answering. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.07630\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.07148\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Design. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.07016\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.06764\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encoding. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.05862\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Let Your Graph Do the Talking: Encoding Structured Data for LLMs. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.05135\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] CADReN: Contextual Anchor-Driven Relational Network for Controllable Cross-Graphs Node Importance Estimation. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.04978\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] An Enhanced Prompt-Based LLM Reasoning Scheme via Knowledge Graph-Integrated Collaboration. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.04627\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] SPARQL Generation: an analysis on fine-tuning OpenLLaMA for Question Answering over a Life Science Knowledge Graph. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.03339\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Interplay of Semantic Communication and Knowledge Learning. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.03299\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] GUARD: Role-playing to Generate Natural-language Jailbreakings to Test Guideline Adherence of Large Language Models. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.02130\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Rendering Graphs for Graph Reasoning in Multimodal Large Language Models. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.01730\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Evaluating LLM -- Generated Multimodal Diagnosis from Medical Images and Symptom Analysis. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.01729\" rel=\"nofollow\"\u003eEACL 2024\u003c/a\u003e] Contextualization Distillation from Large Language Model for Knowledge Graph Completion. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.01495\" rel=\"nofollow\"\u003eEACL 2024\u003c/a\u003e] A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.00414\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Prompt-Time Symbolic Knowledge Capture with Large Language Models. \u003ccode\u003e2024.02\u003c/code\u003e [\u003ca href=\"https://github.com/HaltiaAI/paper-PTSKC\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.00292\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Effective Bug Detection in Graph Database Engines: An LLM-based Approach. \u003ccode\u003e2024.02\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2401.16960\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Two Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignment. \u003ccode\u003e2024.01\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2401.14640\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Benchmarking Large Language Models in Complex Question Answering Attribution using Knowledge Graphs. \u003ccode\u003e2024.01\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2401.13444\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Clue-Guided Path Exploration: An Efficient Knowledge Base Question-Answering Framework with Low Computational Resource Consumption. \u003ccode\u003e2024.01\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2401.12863\" rel=\"nofollow\"\u003eAAAI 2024\u003c/a\u003e] KAM-CoT: Knowledge Augmented Multimodal Chain-of-Thoughts Reasoning. \u003ccode\u003e2024.01\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2401.12671\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Context Matters: Pushing the Boundaries of Open-Ended Answer Generation with Graph-Structured Knowledge Context. \u003ccode\u003e2024.01\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2401.08517\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Supporting Student Decisions on Learning Recommendations: An LLM-Based Chatbot with Knowledge Graph Contextualization for Conversational Explainability and Mentoring. \u003ccode\u003e2024.01\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2401.07237\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Distilling Event Sequence Knowledge From Large Language Models. \u003ccode\u003e2024.01\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2401.06853\" rel=\"nofollow\"\u003eACL 24\u003c/a\u003e] Large Language Models Can Learn Temporal Reasoning. \u003ccode\u003e2024.01\u003c/code\u003e [\u003ca href=\"https://github.com/xiongsiheng/TG-LLM\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2401.06072\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion. \u003ccode\u003e2024.01\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2401.04507\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] TechGPT-2.0: A large language model project to solve the task of knowledge graph construction. \u003ccode\u003e2024.01\u003c/code\u003e [\u003ca href=\"https://github.com/neukg/TechGPT-2.0\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2401.01711\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Evaluating Large Language Models in Semantic Parsing for Conversational Question Answering over Knowledge Graphs. \u003ccode\u003e2024.01\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2401.00761\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] The Earth is Flat? Unveiling Factual Errors in Large Language Models. \u003ccode\u003e2024.01\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2401.00426\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM. \u003ccode\u003e2024.01\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2401.03158\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Quartet Logic: A Four-Step Reasoning (QLFR) framework for advancing Short Text Classification. \u003ccode\u003e2024.01\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2312.17269\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Conversational Question Answering with Reformulations over Knowledge Graph. \u003ccode\u003e2023.12\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2312.15883\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Think and Retrieval: A Hypothesis Knowledge Graph Enhanced Medical Large Language Models. \u003ccode\u003e2023.12\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2312.15880\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] KnowledgeNavigator: Leveraging Large Language Models for Enhanced Reasoning over Knowledge Graph. \u003ccode\u003e2023.12\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2312.11813\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment. \u003ccode\u003e2023.12\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2312.11785\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Zero-Shot Fact-Checking with Semantic Triples and Knowledge Graphs. \u003ccode\u003e2023.12\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2312.11539\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] KGLens: A Parameterized Knowledge Graph Solution to Assess What an LLM Does and Doesn't Know. \u003ccode\u003e2023.12\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2312.11282\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] LLM-ARK: Knowledge Graph Reasoning Using Large Language Models via Deep Reinforcement Learning. \u003ccode\u003e2023.12\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2312.09126\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Towards Trustworthy AI Software Development Assistance. \u003ccode\u003e2023.12\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2312.06185\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] KnowGPT: Black-Box Knowledge Injection for Large Language Models. \u003ccode\u003e2023.12\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2312.05276\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation. \u003ccode\u003e2023.12\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2312.03749\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Conceptual Engineering Using Large Language Models. \u003ccode\u003e2023.12\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2312.03022\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Construction. \u003ccode\u003e2023.12\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2312.01954\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Zero- and Few-Shots Knowledge Graph Triplet Extraction with Large Language Models. \u003ccode\u003e2023.12\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2312.00353\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] On Exploring the Reasoning Capability of Large Language Models with Knowledge Graphs. \u003ccode\u003e2023.11\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2311.17330\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Biomedical knowledge graph-optimized prompt generation for large language models. \u003ccode\u003e2023.11\u003c/code\u003e [\u003ca href=\"https://github.com/BaranziniLab/KG_RAG\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2311.16137\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] A Graph-to-Text Approach to Knowledge-Grounded Response Generation in Human-Robot Interaction. \u003ccode\u003e2023.11\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"http://arxiv.org/abs/2311.01150\" rel=\"nofollow\"\u003eEMNLP 2023\u003c/a\u003e]Revisiting the Knowledge Injection Frameworks. \u003ccode\u003e2023.12\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://aclanthology.org/2023.emnlp-main.143\" rel=\"nofollow\"\u003eEMNLP 2023\u003c/a\u003e]Does the Correctness of Factual Knowledge Matter for Factual Knowledge-Enhanced Pre-trained Language Models? \u003ccode\u003e2023.12\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://aclanthology.org/2023.emnlp-main.228/\" rel=\"nofollow\"\u003eEMNLP 2023\u003c/a\u003e]ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering over Knowledge Graph. \u003ccode\u003e2023.12\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://aclanthology.org/2023.findings-emnlp.580/\" rel=\"nofollow\"\u003eEMNLP 2023 Findings\u003c/a\u003e]KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion. \u003ccode\u003e2023.12\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2311.01862\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] \u003cmath-renderer class=\"js-inline-math\" style=\"display: inline-block\" data-static-url=\"https://github.githubassets.com/static\" data-run-id=\"9b5c46fafee86632afb35d7dcf77cc09\"\u003e$R^3$\u003c/math-renderer\u003e-NL2GQL: A Hybrid Models Approach for for Accuracy Enhancing and Hallucinations Mitigation. \u003ccode\u003e2023.11\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2311.13314\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting. \u003ccode\u003e2023.11\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2305.14202\" rel=\"nofollow\"\u003eEMNLP 2023\u003c/a\u003e] Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata. \u003ccode\u003e2023.11\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2311.09841\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Leveraging LLMs in Scholarly Knowledge Graph Question Answering. \u003ccode\u003e2023.11\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2311.06503\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering. \u003ccode\u003e2023.11\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2311.03837\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] OLaLa: Ontology Matching with Large Language Models. \u003ccode\u003e2023.11\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2311.02956\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] In-Context Learning for Knowledge Base Question Answering for Unmanned Systems based on Large Language Models. \u003ccode\u003e2023.11\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2311.01266\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Let's Discover More API Relations: A Large Language Model-based AI Chain for Unsupervised API Relation Inference. \u003ccode\u003e2023.11\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2311.00444\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirements. \u003ccode\u003e2023.11\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.02166\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Large Language Models Meet Knowledge Graphs to Answer Factoid Questions. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.07008\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Answer Candidate Type Selection: Text-to-Text Language Model for Closed Book Question Answering Meets Knowledge Graphs. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2311.00287\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.20170\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.19998\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.18951\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] A Multimodal Ecological Civilization Pattern Recommendation Method Based on Large Language Models and Knowledge Graph. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.18356\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.16421\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Graph Agent: Explicit Reasoning Agent for Graphs. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.14174\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] An In-Context Schema Understanding Method for Knowledge Base Question Answering. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.13023\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] GraphGPT: Graph Instruction Tuning for Large Language Models. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.11638\" rel=\"nofollow\"\u003eEMNLP 2023 Findings\u003c/a\u003e] Systematic Assessment of Factual Knowledge in Large Language Models. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.11220\" rel=\"nofollow\"\u003eEMNLP 2023 Findings\u003c/a\u003e] KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.10445\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalities. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.09089\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.08975\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.08365\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] From Large Language Models to Knowledge Graphs for Biomarker Discovery in Cancer. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.06671\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Making Large Language Models Perform Better in Knowledge Graph Completion. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.08279\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] CP-KGC: Constrained-Prompt Knowledge Graph Completion with Large Language Models. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.07170\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] PHALM: Building a Knowledge Graph from Scratch by Prompting Humans and a Language Model. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.03269\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] InstructProtein: Aligning Human and Protein Language via Knowledge Instruction. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.01290\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Knowledge Crosswords: Geometric Reasoning over Structured Knowledge with Large Language Models. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.01061\" rel=\"nofollow\"\u003eICLR 2024\u003c/a\u003e] Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning. \u003ccode\u003e2023.10\u003c/code\u003e [\u003ca href=\"https://github.com/RManLuo/reasoning-on-graphs\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.00299\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] RelBERT: Embedding Relations with Language Models. \u003ccode\u003e2023.10\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2309.17122\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?. \u003ccode\u003e2023.09\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/pdf/2309.16134\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Let's Chat to Find the APIs: Connecting Human, LLM and Knowledge Graph through AI Chain. \u003ccode\u003e2023.09\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/pdf/2309.15427\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Graph Neural Prompting with Large Language Models. \u003ccode\u003e2023.09\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2309.12132\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] A knowledge representation approach for construction contract knowledge modeling. \u003ccode\u003e2023.09\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2309.11206\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering. \u003ccode\u003e2023.09\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2309.08594\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] \"Merge Conflicts!\" Exploring the Impacts of External Distractors to Parametric Knowledge Graphs. \u003ccode\u003e2023.09\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2309.00240\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking. \u003ccode\u003e2023.09\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/pdf/2309.01538\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning. \u003ccode\u003e2023.09\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2309.04695\" rel=\"nofollow\"\u003eAAAI 2024\u003c/a\u003e] Code-Style In-Context Learning for Knowledge-Based Question Answering. \u003ccode\u003e2023.09\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2309.04565\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Unleashing the Power of Graph Learning through LLM-based Autonomous Agents. \u003ccode\u003e2023.09\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2309.04175\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Reliable Response Generation in Chinese. \u003ccode\u003e2023.09\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2309.03118\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from Knowledge Graphs. \u003ccode\u003e2023.09\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2308.14429\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Biomedical Entity Linking with Triple-aware Pre-Training. \u003ccode\u003e2023.08\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2308.13916\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Exploring Large Language Models for Knowledge Graph Completion. \u003ccode\u003e2023.08\u003c/code\u003e [\u003ca href=\"https://github.com/yao8839836/kg-llm\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2308.16622\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering. \u003ccode\u003e2023.08\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2308.14321\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Leveraging A Medical Knowledge Graph into Large Language Models for Diagnosis Prediction. \u003ccode\u003e2023.08\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2308.12028\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] LKPNR: LLM and KG for Personalized News Recommendation Framework. \u003ccode\u003e2023.08\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2308.11730\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Knowledge Graph Prompting for Multi-Document Question Answering. \u003ccode\u003e2023.08\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2308.10168\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Head-to-Tail: How Knowledgeable are Large Language Models (LLM)? A.K.A. Will LLMs Replace Knowledge Graphs?. \u003ccode\u003e2023.08\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2308.09729\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models. \u003ccode\u003e2023.08\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2308.00081\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Towards Semantically Enriched Embeddings for Knowledge Graph Completion. \u003ccode\u003e2023.07\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2307.11772\" rel=\"nofollow\"\u003eTKDE 2024\u003c/a\u003e] AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models. \u003ccode\u003e2023.07\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2307.07312\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge Graphs. \u003ccode\u003e2023.07\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2307.07697\" rel=\"nofollow\"\u003eICLR 2024\u003c/a\u003e] Think-on-Graph: Deep and Responsible Reasoning of Large Language Model with Knowledge Graph. \u003ccode\u003e2023.07\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2307.03393\" rel=\"nofollow\"\u003eSIGKDD 2024 Explorations\u003c/a\u003e] Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs. \u003ccode\u003e2023.07\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2307.05722\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations. \u003ccode\u003e2023.07\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2307.02738\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] RecallM: An Architecture for Temporal Context Understanding and Question Answering. \u003ccode\u003e2023.07\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2307.06917\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT. \u003ccode\u003e2023.07\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2307.01128\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction. \u003ccode\u003e2023.07\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2306.10241\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model\n. \u003ccode\u003e2023.06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/pdf/2306.04136.pdf\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering. \u003ccode\u003e2023.06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2306.10723\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Fine-tuning Large Enterprise Language Models via Ontological Reasoning. \u003ccode\u003e2023.06\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2305.18395\" rel=\"nofollow\"\u003eNeurIPS 2023\u003c/a\u003e] Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks. \u003ccode\u003e2023.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/pdf/2305.04676\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Enhancing Knowledge Graph Construction Using Large Language Models. \u003ccode\u003e2023.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2305.03513\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs. \u003ccode\u003e2023.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2305.06590\" rel=\"nofollow\"\u003eACL 2023\u003c/a\u003e] FactKG: Fact Verification via Reasoning on Knowledge Graphs. \u003ccode\u003e2023.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2304.05973\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting. \u003ccode\u003e2023.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2305.09645\" rel=\"nofollow\"\u003eEMNLP 2023\u003c/a\u003e] StructGPT: A General Framework for Large Language Model to Reason over Structured Data. \u003ccode\u003e2023.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2305.19523\" rel=\"nofollow\"\u003eICLR 2024\u003c/a\u003e] Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning. \u003ccode\u003e2023.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2305.13168\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities. \u003ccode\u003e2023.05\u003c/code\u003e [\u003ca href=\"https://github.com/zjunlp/AutoKG\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2305.10037\" rel=\"nofollow\"\u003eNeurIPS 2023\u003c/a\u003e] Can Language Models Solve Graph Problems in Natural Language? \u003ccode\u003e2023.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2305.09858\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs. \u003ccode\u003e2023.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2305.16755\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Can large language models generate salient negative statements? \u003ccode\u003e2023.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2305.15066\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking. \u003ccode\u003e2023.05\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2305.01157\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Complex Logical Reasoning over Knowledge Graphs using Large Language Models. \u003ccode\u003e2023.05\u003c/code\u003e [\u003ca href=\"https://github.com/Akirato/LLM-KG-Reasoning/tree/main\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2305.00050\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Causal Reasoning and Large Language Models: Opening a New Frontier for Causality. \u003ccode\u003e2023.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2303.05279\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Can large language models build causal graphs? \u003ccode\u003e2023.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2304.05774\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Using Multiple RDF Knowledge Graphs for Enriching ChatGPT Responses. \u003ccode\u003e2023.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2304.11116\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT. \u003ccode\u003e2023.04\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2304.02711\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning. \u003ccode\u003e2023.04\u003c/code\u003e [\u003ca href=\"https://github.com/monarch-initiative/ontogpt\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eResources and Benchmarking\u003c/h3\u003e\u003ca id=\"user-content-resources-and-benchmarking\" class=\"anchor\" aria-label=\"Permalink: Resources and Benchmarking\" href=\"#resources-and-benchmarking\"\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=\"https://arxiv.org/abs/2404.13207\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases. \u003ccode\u003e2024.04\u003c/code\u003e [\u003ca href=\"https://github.com/snap-stanford/stark\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2402.06341\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] RareBench: Can LLMs Serve as Rare Diseases Specialists?. \u003ccode\u003e2024.02\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2401.14640\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Benchmarking Large Language Models in Complex Question Answering Attribution using Knowledge Graphs. \u003ccode\u003e2024.01\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2401.06853\" rel=\"nofollow\"\u003eACL 24\u003c/a\u003e] Large Language Models Can Learn Temporal Reasoning. \u003ccode\u003e2024.01\u003c/code\u003e [\u003ca href=\"https://github.com/xiongsiheng/TG-LLM\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2311.09174\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph. \u003ccode\u003e2023.11\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2311.07509\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases. \u003ccode\u003e2023.11\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.05634\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Towards Verifiable Generation: A Benchmark for Knowledge-aware Language Model Attribution. \u003ccode\u003e2023.10\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.15517\" rel=\"nofollow\"\u003eEMNLP 2023\u003c/a\u003e] MarkQA: A large scale KBQA dataset with numerical reasoning. \u003ccode\u003e2023.10\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2306.14704\" rel=\"nofollow\"\u003eCIKM 2023\u003c/a\u003e] Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement. \u003ccode\u003e2023.06\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2306.05783\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation. \u003ccode\u003e2023.06\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2210.00305\" rel=\"nofollow\"\u003eAACL 2023 System Demonstrations\u003c/a\u003e] LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings \u003ccode\u003e2023.03\u003c/code\u003e [\u003ca href=\"http://47.92.96.190:9001/\" rel=\"nofollow\"\u003eRepo\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2309.11669\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] Construction of Paired Knowledge Graph-Text Datasets Informed by Cyclic Evaluation. \u003ccode\u003e2023.09\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2310.08365\" rel=\"nofollow\"\u003earxiv\u003c/a\u003e] From Large Language Models to Knowledge Graphs for Biomarker Discovery in Cancer. \u003ccode\u003e2023.10\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e[\u003ca href=\"https://arxiv.org/abs/2308.02357\" rel=\"nofollow\"\u003eISWC 2023\u003c/a\u003e] Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text. \u003ccode\u003e2023.08\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContribution\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\"\u003e👥 Contributors\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/zjukg/KG-LLM-Papers/graphs/contributors\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/2587b3dcfd9b268a898efd413cf2fe89756c0600753691bd764a9e1402499694/68747470733a2f2f636f6e747269622e726f636b732f696d6167653f7265706f3d7a6a756b672f4b472d4c4c4d2d506170657273\" data-canonical-src=\"https://contrib.rocks/image?repo=zjukg/KG-LLM-Papers\" 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\"\u003e🎉 Contributing ( welcome ! )\u003c/h3\u003e\u003ca id=\"user-content--contributing--welcome--\" class=\"anchor\" aria-label=\"Permalink: 🎉 Contributing ( welcome ! )\" href=\"#-contributing--welcome--\"\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✨ Add a new paper or update an existing KG-related LLM paper.\u003c/li\u003e\n\u003cli\u003e🧐 Use the same format as existing entries to describe the work.\u003c/li\u003e\n\u003cli\u003e😄 A very brief explanation why you think a paper should be added or updated is recommended (Not Neccessary) via \u003cstrong\u003e\u003ccode\u003eAdding Issues\u003c/code\u003e\u003c/strong\u003e or \u003cstrong\u003e\u003ccode\u003ePull Requests\u003c/code\u003e\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDon't worry if you put something wrong, they will be fixed for you. Just feel free to contribute and promote your awesome work here! 🤩 We'll get back to you in time ~ 😉\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a63dd8f593a6536ad4b7ce5978d689ef71596be697885c81d62da490d2eb87cd/68747470733a2f2f6170692e737461722d686973746f72792e636f6d2f7376673f7265706f733d7a6a756b672f4b472d4c4c4d2d50617065727326747970653d44617465\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a63dd8f593a6536ad4b7ce5978d689ef71596be697885c81d62da490d2eb87cd/68747470733a2f2f6170692e737461722d686973746f72792e636f6d2f7376673f7265706f733d7a6a756b672f4b472d4c4c4d2d50617065727326747970653d44617465\" alt=\"Star History Chart\" data-canonical-src=\"https://api.star-history.com/svg?repos=zjukg/KG-LLM-Papers\u0026amp;type=Date\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🤝 Cite:\u003c/h2\u003e\u003ca id=\"user-content--cite\" class=\"anchor\" aria-label=\"Permalink: 🤝 Cite:\" href=\"#-cite\"\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\"\u003eIf this Repo is helpful to you, please consider citing one of our papers. We would greatly appreciate it :)\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@inproceedings{DBLP:conf/nips/GuoB000LSZLLZZC24,\n author = {Lingbing Guo and\n Zhongpu Bo and\n Zhuo Chen and\n Yichi Zhang and\n Jiaoyan Chen and\n Yarong Lan and\n Mengshu Sun and\n Zhiqiang Zhang and\n Yangyifei Luo and\n Qian Li and\n Qiang Zhang and\n Wen Zhang and\n Huajun Chen},\n title = {{MKGL:} Mastery of a Three-Word Language},\n booktitle = {NeurIPS},\n year = {2024}\n}\"\u003e\u003cpre lang=\"bigquery\" class=\"notranslate\"\u003e\u003ccode\u003e@inproceedings{DBLP:conf/nips/GuoB000LSZLLZZC24,\n author = {Lingbing Guo and\n Zhongpu Bo and\n Zhuo Chen and\n Yichi Zhang and\n Jiaoyan Chen and\n Yarong Lan and\n Mengshu Sun and\n Zhiqiang Zhang and\n Yangyifei Luo and\n Qian Li and\n Qiang Zhang and\n Wen Zhang and\n Huajun Chen},\n title = {{MKGL:} Mastery of a Three-Word Language},\n booktitle = {NeurIPS},\n year = {2024}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@inproceedings{DBLP:conf/acl/ZhangCFLL0C24,\n author = {Yichi Zhang and\n Zhuo Chen and\n Yin Fang and\n Yanxi Lu and\n Fangming Li and\n Wen Zhang and\n Huajun Chen},\n title = {Knowledgeable Preference Alignment for LLMs in Domain-specific Question\n Answering},\n booktitle = {{ACL} (Findings)},\n pages = {891--904},\n publisher = {Association for Computational Linguistics},\n year = {2024}\n}\n\n\"\u003e\u003cpre lang=\"bigquery\" class=\"notranslate\"\u003e\u003ccode\u003e@inproceedings{DBLP:conf/acl/ZhangCFLL0C24,\n author = {Yichi Zhang and\n Zhuo Chen and\n Yin Fang and\n Yanxi Lu and\n Fangming Li and\n Wen Zhang and\n Huajun Chen},\n title = {Knowledgeable Preference Alignment for LLMs in Domain-specific Question\n Answering},\n booktitle = {{ACL} (Findings)},\n pages = 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{{ACM}},\n year = {2024}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"KG-LLM-Papers","anchor":"kg-llm-papers","htmlText":"KG-LLM-Papers"},{"level":2,"text":"Content","anchor":"content","htmlText":"Content"},{"level":2,"text":"Papers","anchor":"papers","htmlText":"Papers"},{"level":3,"text":"Surveys","anchor":"surveys","htmlText":"Surveys"},{"level":3,"text":"Method","anchor":"method","htmlText":"Method"},{"level":3,"text":"Resources and Benchmarking","anchor":"resources-and-benchmarking","htmlText":"Resources and Benchmarking"},{"level":2,"text":"Contribution","anchor":"contribution","htmlText":"Contribution"},{"level":3,"text":"👥 Contributors","anchor":"-contributors","htmlText":"👥 Contributors"},{"level":3,"text":"🎉 Contributing ( welcome ! )","anchor":"-contributing--welcome--","htmlText":"🎉 Contributing ( welcome ! )"},{"level":2,"text":"🤝 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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/zjukg/KG-LLM-Papers"><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://github.com/zjukg/KG-LLM-Papers/blob/main/LICENSE"><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/1c14b1f572078d9ce103e23337a6f10bc6bba51f1f3c08992f68bb9ff2c6af6e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f7a6a756b672f4b472d4c4c4d2d5061706572733f636f6c6f723d677265656e"><img src="https://camo.githubusercontent.com/1c14b1f572078d9ce103e23337a6f10bc6bba51f1f3c08992f68bb9ff2c6af6e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f7a6a756b672f4b472d4c4c4d2d5061706572733f636f6c6f723d677265656e" alt="" data-canonical-src="https://img.shields.io/github/last-commit/zjukg/KG-LLM-Papers?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> <blockquote> <p dir="auto">What can LLMs do for KGs? Or, in other words, what role can KG play in the era of LLMs?</p> </blockquote> <p dir="auto">🙌 This repository collects papers integrating <strong>knowledge graphs (KGs)</strong> and <strong>large language models (LLMs)</strong>.</p> <p dir="auto">😎 Welcome to recommend missing papers through <strong><code>Pull Requests</code></strong>.</p> <details> <summary>👈 🔔 News </summary> <ul dir="auto"> <li><strong><code>2025-02</code> We preprint our Paper <a href="https://arxiv.org/abs/2502.05478" rel="nofollow">OntoTune: Ontology-Driven Self-training for Aligning Large Language Models</a> (WWW 2025) [<a href="https://github.com/zjukg/OntoTune"><code>Repo</code></a>].</strong></li> <li><strong><code>2025-02</code> We preprint our Paper <a href="https://arxiv.org/abs/2502.06257" rel="nofollow">K-ON: Stacking Knowledge On the Head Layer of Large Language Model</a> (AAAI 2025 Oral) [<a href="https://github.com/zjukg/K-ON"><code>Repo</code></a>].</strong></li> <li><strong><code>2025-01</code> We preprint our Paper <a href="https://arxiv.org/abs/2501.00244" rel="nofollow">Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking</a> [<a href="https://github.com/zjukg/SUBARU"><code>Repo</code></a>].</strong></li> <li><strong><code>2024-12</code> We preprint our Paper <a href="https://arxiv.org/abs/2406.18916" rel="nofollow">TrustUQA: A Trustful Framework for Unified Structured Data Question Answering</a> (AAAI 2025) [<a href="https://github.com/zjukg/TrustUQA"><code>Repo</code></a>].</strong></li> <li><strong><code>2024-09</code> Our paper <a href="https://openreview.net/forum?id=eqMNwXvOqn" rel="nofollow">MKGL: Mastery of a Three-Word Language</a> has been accepted by NeurIPS 2024 as a spotlight paper. [<a href="https://github.com/zjukg/MKGL"><code>Repo</code></a>]</strong></li> <li><strong><code>2024-07</code> Our paper <a href="https://arxiv.org/abs/2310.06671" rel="nofollow">Making Large Language Models Perform Better in Knowledge Graph Completion</a> has been accepted by ACM MM 2024 as an oral paper. [<a href="https://github.com/zjukg/KoPA"><code>Repo</code></a>]</strong></li> <li><strong><code>2024-05</code> Our paper <a href="https://arxiv.org/abs/2311.06503" rel="nofollow">Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering</a> has been accepted by ACL 2024. [<a href="https://github.com/zjukg/KnowPAT"><code>Repo</code></a>]</strong></li> <li><strong><code>2024-02</code> We preprint our Survey <a href="http://arxiv.org/abs/2402.05391" rel="nofollow">Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey</a> [<a href="https://github.com/zjukg/KG-MM-Survey"><code>Repo</code></a>].</strong></li> <li><strong><code>2023-06</code> We create this repository to maintain a paper list on <code>Intergrating Knowledge Graphs and Large Language Models</code>.</strong></li> </ul> </details> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Content</h2><a id="user-content-content" class="anchor" aria-label="Permalink: Content" href="#content"><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="#papers">📜 Papers</a> <ul dir="auto"> <li><a href="#surveys">🔖 Surveys</a></li> <li><a href="#methods">⚙ Methods</a></li> <li><a href="#resources-and-benchmarking">🧰 Resources</a></li> </ul> </li> </ul> <hr> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Papers</h2><a id="user-content-papers" class="anchor" aria-label="Permalink: Papers" href="#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> <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> <ul dir="auto"> <li>[<a href="https://www.sciencedirect.com/science/article/pii/S1570826824000301" rel="nofollow">JoWS</a>] Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective <code>2024.12</code></li> <li>[<a href="https://arxiv.org/abs/2402.05391" rel="nofollow">arxiv</a>] Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey. <code>2024.02</code></li> <li>[<a href="https://arxiv.org/abs/2311.07914" rel="nofollow">arxiv</a>] Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey. <code>2023.11</code></li> <li>[<a href="https://arxiv.org/abs/2310.07521" rel="nofollow">arxiv</a>] Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity. <code>2023.10</code></li> <li>[<a href="https://arxiv.org/abs/2310.04835" rel="nofollow">arxiv</a>] On the Evolution of Knowledge Graphs: A Survey and Perspective. <code>2023.10</code></li> <li>[<a href="https://arxiv.org/pdf/2309.17122" rel="nofollow">arxiv</a>] Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle? <code>2023.09</code></li> <li>[<a href="https://arxiv.org/abs/2309.01029" rel="nofollow">arxiv</a>] Explainability for Large Language Models: A Survey. <code>2023.09</code></li> <li>[<a href="https://arxiv.org/abs/2308.14217" rel="nofollow">arxiv</a>] Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact. <code>2023.08</code></li> <li>[<a href="https://arxiv.org/abs/2308.06374" rel="nofollow">arxiv</a>] Large Language Models and Knowledge Graphs: Opportunities and Challenges. <code>2023.08</code></li> <li>[<a href="https://arxiv.org/pdf/2306.08302" rel="nofollow">TKDE</a>] Unifying Large Language Models and Knowledge Graphs: A Roadmap. <code>2023.06</code> [<a href="https://github.com/RManLuo/Awesome-LLM-KG">Repo</a>]</li> <li>[<a href="https://arxiv.org/pdf/2306.11489.pdf" rel="nofollow">arxiv</a>] ChatGPT is not Enough: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling. <code>2023.06</code></li> <li>[<a href="https://arxiv.org/abs/2211.05994" rel="nofollow">arxiv</a>] A Survey of Knowledge-Enhanced Pre-trained Language Models. <code>2023.05</code></li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Method</h3><a id="user-content-method" class="anchor" aria-label="Permalink: Method" href="#method"><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="https://arxiv.org/abs/2502.10453" rel="nofollow">arxiv</a>] Linking Cryptoasset Attribution Tags to Knowledge Graph Entities: An LLM-based Approach. <code>2025.2</code> </li> <li>[<a href="https://arxiv.org/abs/2502.05478" rel="nofollow">arxiv</a>] OntoTune: Ontology-Driven Self-training for Aligning Large Language Models. <code>2025.2</code> </li> <li>[<a href="https://aclanthology.org/2024.findings-emnlp.524/" rel="nofollow">EMNLP 2024 findings</a>] Question-guided Knowledge Graph Re-scoring and Injection for Knowledge Graph Question Answering. <code>2024.11</code> </li> <li>[<a href="https://arxiv.org/abs/2407.11417" rel="nofollow">EMNLP 2024 findings</a>] SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions. <code>2024.11</code> </li> <li>[<a href="https://arxiv.org/pdf/2410.18415" rel="nofollow">arxiv</a>] Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains. <code>2024.10</code> </li> <li>[<a href="https://arxiv.org/abs/2410.12609" rel="nofollow">arxiv</a>] Towards Graph Foundation Models: The Perspective of Zero-shot Reasoning on Knowledge Graphs. <code>2024.10</code> </li> <li>[<a href="https://arxiv.org/abs/2410.07526" rel="nofollow">NeurIPS 2024</a>] MKGL: Mastery of a Three-Word Language. <code>2024.10</code> [<a href="https://github.com/zjukg/MKGL">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2402.06861" rel="nofollow">NeurIPS 2024</a>] UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Construction. <code>2024.10</code> [<a href="https://github.com/usail-hkust/UrbanKGent">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2405.16412" rel="nofollow">NeurIPS 2024</a>] KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge. <code>2024.10</code> [<a href="https://github.com/pat-jj/KG-FIT">Repo</a>]</li> <li>[<a href="https://openreview.net/forum?id=JCG0KTPVYy" rel="nofollow">ICML 2024</a>] Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models. <code>2024.10</code> [<a href="https://github.com/shiliu-egg/ICML2024_COFT">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2410.02811" rel="nofollow">ACL 2024</a>] SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graphs. <code>2024.09</code> </li> <li>[<a href="https://arxiv.org/abs/2405.16806" rel="nofollow">NeurIPS 2024</a>] LLM4EA: Entity Alignment with Noisy Annotations from Large Language Models. <code>2024.09</code> [<a href="https://github.com/chensyCN/llm4ea_official">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2407.00653" rel="nofollow">arxiv</a>] Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language Models by Learning from Knowledge Graphs. <code>2024.07</code> </li> <li>[<a href="https://arxiv.org/abs/2407.10793" rel="nofollow">arxiv</a>] GraphEval: A Knowledge-Graph Based LLM Hallucination Evaluation Framework. <code>2024.07</code> </li> <li>[<a href="https://arxiv.org/abs/2407.10805" rel="nofollow">arxiv</a>] Think-on-Graph 2.0: Deep and Interpretable Large Language Model Reasoning with Knowledge Graph-guided Retrieval. <code>2024.07</code> </li> <li>[<a href="https://arxiv.org/abs/2407.16127" rel="nofollow">ISWC 2024</a>] Finetuning Generative Large Language Models with Discrimination Instructions for Knowledge Graph Completion. <code>2024.07</code> </li> <li>[<a href="https://arxiv.org/abs/2402.16568" rel="nofollow">ACL 2024 findings</a>] Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models. <code>2024.07</code> </li> <li>[<a href="https://arxiv.org/abs/2407.21358" rel="nofollow">arxiv</a>] Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs. <code>2024.07</code> </li> <li>[<a href="https://arxiv.org/abs/2310.07793" rel="nofollow">NAACL 2024 findings</a>] GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models. <code>2024.06</code> </li> <li>[<a href="https://arxiv.org/abs/2402.11199" rel="nofollow">ACL 2024 findings</a>] Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models. <code>2024.06</code> </li> <li>[<a href="https://arxiv.org/abs/2406.03746" rel="nofollow">arxiv</a>] Efficient Knowledge Infusion via KG-LLM Alignment. <code>2024.06</code> </li> <li>[<a href="https://arxiv.org/abs/2406.18114" rel="nofollow">arxiv</a>] Knowledge Graph Enhanced Retrieval-Augmented Generation for Failure Mode and Effects Analysis. <code>2024.06</code> </li> <li>[<a href="https://arxiv.org/abs/2406.13862" rel="nofollow">arxiv</a>] Knowledge Graph-Enhanced Large Language Models via Path Selection. <code>2024.06</code> </li> <li>[<a href="https://arxiv.org/abs/2406.14282" rel="nofollow">arxiv</a>] Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs. <code>2024.06</code> </li> <li>[<a href="https://arxiv.org/abs/2406.02962" rel="nofollow">arxiv</a>] Docs2KG: Unified Knowledge Graph Construction from Heterogeneous Documents Assisted by Large Language Models. <code>2024.06</code> </li> <li>[<a href="https://arxiv.org/abs/2406.02110" rel="nofollow">arxiv</a>] UniOQA: A Unified Framework for Knowledge Graph Question Answering with Large Language Model. <code>2024.06</code> </li> <li>[<a href="https://arxiv.org/abs/2406.02030" rel="nofollow">arxiv</a>] Multimodal Reasoning with Multimodal Knowledge Graph. <code>2024.06</code> </li> <li>[<a href="https://arxiv.org/abs/2406.01391" rel="nofollow">arxiv</a>] Knowledge Graph in Astronomical Research with Large Language Models: Quantifying Driving Forces in Interdisciplinary Scientific Discovery. <code>2024.06</code> </li> <li>[<a href="https://arxiv.org/abs/2406.01238" rel="nofollow">arxiv</a>] EffiQA: Efficient Question-Answering with Strategic Multi-Model Collaboration on Knowledge Graphs. <code>2024.06</code> </li> <li>[<a href="https://arxiv.org/abs/2406.01145" rel="nofollow">arxiv</a>] Explore then Determine: A GNN-LLM Synergy Framework for Reasoning over Knowledge Graph. <code>2024.06</code> </li> <li>[<a href="https://arxiv.org/abs/2406.00036" rel="nofollow">arxiv</a>] EMERGE: Integrating RAG for Improved Multimodal EHR Predictive Modeling. <code>2024.06</code> </li> <li>[<a href="https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-024-00481-2" rel="nofollow">EPJ Data Science</a>] Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models. <code>2024.06</code> </li> <li>[<a href="https://arxiv.org/abs/2405.20455" rel="nofollow">arxiv</a>] DepsRAG: Towards Managing Software Dependencies using Large Language Models. <code>2024.06</code> [<a href="https://github.com/Mohannadcse/DepsRAG">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2405.19877" rel="nofollow">arxiv</a>] KNOW: A Real-World Ontology for Knowledge Capture with Large Language Models <code>2024.05</code> [<a href="https://github.com/KnowOntology">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2405.14831" rel="nofollow">arxiv</a>] HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models <code>2024.05</code> [<a href="https://github.com/OSU-NLP-Group/HippoRAG">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2405.14012" rel="nofollow">arxiv</a>] Prompt-Time Ontology-Driven Symbolic Knowledge Capture with Large Language Models <code>2024.05</code> [<a href="https://github.com/HaltiaAI/paper-PTODSKC">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2405.10288" rel="nofollow">arxiv</a>] Timeline-based Sentence Decomposition with In-Context Learning for Temporal Fact Extraction. <code>2024.05</code> </li> <li>[<a href="https://arxiv.org/abs/2405.09713" rel="nofollow">arxiv</a>] SOK-Bench: A Situated Video Reasoning Benchmark with Aligned Open-World Knowledge. <code>2024.05</code> </li> <li>[<a href="https://arxiv.org/abs/2405.06545" rel="nofollow">arxiv</a>] Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrieval. <code>2024.05</code> </li> <li>[<a href="https://arxiv.org/abs/2405.06524" rel="nofollow">arxiv</a>] Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts. <code>2024.05</code> </li> <li>[<a href="https://arxiv.org/abs/2405.04819" rel="nofollow">arxiv</a>] DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature. <code>2024.05</code> </li> <li>[<a href="https://arxiv.org/abs/2405.04756" rel="nofollow">arxiv</a>] BiasKG: Adversarial Knowledge Graphs to Induce Bias in Large Language Models. <code>2024.05</code> </li> <li>[<a href="https://arxiv.org/abs/2405.04753" rel="nofollow">arxiv</a>] AttacKG+:Boosting Attack Knowledge Graph Construction with Large Language Models. <code>2024.05</code> </li> <li>[<a href="https://arxiv.org/abs/2405.04180" rel="nofollow">arxiv</a>] Sora Detector: A Unified Hallucination Detection for Large Text-to-Video Models. <code>2024.05</code> </li> <li>[<a href="https://arxiv.org/abs/2405.03734" rel="nofollow">arxiv</a>] FOKE: A Personalized and Explainable Education Framework Integrating Foundation Models, Knowledge Graphs, and Prompt Engineering. <code>2024.05</code> </li> <li>[<a href="https://arxiv.org/abs/2405.02738" rel="nofollow">arxiv</a>] Relations Prediction for Knowledge Graph Completion using Large Language Models. <code>2024.05</code> </li> <li>[<a href="https://arxiv.org/abs/2405.02105" rel="nofollow">arxiv</a>] Evaluating Large Language Models for Structured Science Summarization in the Open Research Knowledge Graph. <code>2024.05</code> </li> <li>[<a href="https://arxiv.org/abs/2405.01649" rel="nofollow">arxiv</a>] Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning. <code>2024.05</code> </li> <li>[<a href="https://arxiv.org/abs/2405.00449" rel="nofollow">arxiv</a>] RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models. <code>2024.05</code> </li> <li>[<a href="https://arxiv.org/abs/2404.19744" rel="nofollow">arxiv</a>] PrivComp-KG : Leveraging Knowledge Graph and Large Language Models for Privacy Policy Compliance Verification. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.19234" rel="nofollow">arxiv</a>] Multi-hop Question Answering over Knowledge Graphs using Large Language Models. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.19146" rel="nofollow">arxiv</a>] Automated Construction of Theme-specific Knowledge Graphs. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.17723" rel="nofollow">arxiv</a>] Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.17000" rel="nofollow">arxiv</a>] Evaluating Class Membership Relations in Knowledge Graphs using Large Language Models. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.15923" rel="nofollow">arxiv</a>] KGValidator: A Framework for Automatic Validation of Knowledge Graph Construction. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.13865" rel="nofollow">arxiv</a>] Context-Enhanced Language Models for Generating Multi-Paper Citations. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.10384" rel="nofollow">arxiv</a>] Reasoning on Efficient Knowledge Paths:Knowledge Graph Guides Large Language Model for Domain Question Answering. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.09763" rel="nofollow">arxiv</a>] KG-CTG: Citation Generation through Knowledge Graph-guided Large Language Models. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.09077" rel="nofollow">arxiv</a>] CuriousLLM: Elevating Multi-Document QA with Reasoning-Infused Knowledge Graph Prompting. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.07677" rel="nofollow">arxiv</a>] ODA: Observation-Driven Agent for integrating LLMs and Knowledge Graphs. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.06571" rel="nofollow">arxiv</a>] Building A Knowledge Graph to Enrich ChatGPT Responses in Manufacturing Service Discovery. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.04264" rel="nofollow">arxiv</a>] Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.03868" rel="nofollow">arxiv</a>] Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction. <code>2024.04</code> </li> <li>[<a href="https://openreview.net/forum?id=dWYRjT501w" rel="nofollow">COLM 2024</a>] Unveiling LLMs: The Evolution of Latent Representations in a Dynamic Knowledge Graph. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.03080" rel="nofollow">arxiv</a>] Construction of Functional Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.02389" rel="nofollow">arxiv</a>] On Linearizing Structured Data in Encoder-Decoder Language Models: Insights from Text-to-SQL. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.01720" rel="nofollow">arxiv</a>] Self-Improvement Programming for Temporal Knowledge Graph Question Answering. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.01425" rel="nofollow">arxiv</a>] A Preliminary Roadmap for LLMs as Assistants in Exploring, Analyzing, and Visualizing Knowledge Graphs. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.00942" rel="nofollow">arxiv</a>] Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.00589" rel="nofollow">arxiv</a>] Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.00209" rel="nofollow">arxiv</a>] EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.14741" rel="nofollow">arxiv</a>] Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2404.16130" rel="nofollow">arxiv</a>] From Local to Global: A Graph RAG Approach to Query-Focused Summarization. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2312.15883" rel="nofollow">arxiv</a>] HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses. <code>2024.04</code> </li> <li>[<a href="https://arxiv.org/abs/2403.14950" rel="nofollow">arxiv</a>] KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.17532" rel="nofollow">LREC-COLING 2024</a>] KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.14253" rel="nofollow">arxiv</a>] K-Act2Emo: Korean Commonsense Knowledge Graph for Indirect Emotional Expression. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.12151" rel="nofollow">arxiv</a>] Fusing Domain-Specific Content from Large Language Models into Knowledge Graphs for Enhanced Zero Shot Object State Classification. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.11786" rel="nofollow">arxiv</a>] Construction of Hyper-Relational Knowledge Graphs Using Pre-Trained Large Language Models. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.08593" rel="nofollow">arxiv</a>] Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.08345" rel="nofollow">arxiv</a>] From human experts to machines: An LLM supported approach to ontology and knowledge graph construction. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.07398" rel="nofollow">arxiv</a>] Complex Reasoning over Logical Queries on Commonsense Knowledge Graphs. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.07311" rel="nofollow">arxiv</a>] Knowledge Graph Large Language Model (KG-LLM) for Link Prediction. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.05881" rel="nofollow">arxiv</a>] KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.04261" rel="nofollow">arxiv</a>] Advancing Biomedical Text Mining with Community Challenges. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.03008" rel="nofollow">arxiv</a>] Knowledge Graphs as Context Sources for LLM-Based Explanations of Learning Recommendations. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.02966" rel="nofollow">arxiv</a>] Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.02576" rel="nofollow">arxiv</a>] AceMap: Knowledge Discovery through Academic Graph. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.02253" rel="nofollow">arxiv</a>] KnowPhish: Large Language Models Meet Multimodal Knowledge Graphs for Enhancing Reference-Based Phishing Detection. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.02014" rel="nofollow">arxiv</a>] Unveiling Hidden Links Between Unseen Security Entities. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.01972" rel="nofollow">LREC-COLING 2024</a>] Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.01481" rel="nofollow">arxiv</a>] Infusing Knowledge into Large Language Models with Contextual Prompts. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.01395" rel="nofollow">arxiv</a>] CR-LT-KGQA: A Knowledge Graph Question Answering Dataset Requiring Commonsense Reasoning and Long-Tail Knowledge. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.01390" rel="nofollow">arxiv</a>] Right for Right Reasons: Large Language Models for Verifiable Commonsense Knowledge Graph Question Answering. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.01382" rel="nofollow">arxiv</a>] Automatic Question-Answer Generation for Long-Tail Knowledge. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2403.00953" rel="nofollow">arxiv</a>] AutoRD: An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontologies-enhanced Large Language Models. <code>2024.03</code> </li> <li>[<a href="https://arxiv.org/abs/2402.17786" rel="nofollow">arxiv</a>] Stepwise Self-Consistent Mathematical Reasoning with Large Language Models. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.16568" rel="nofollow">arxiv</a>] Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.15048" rel="nofollow">arxiv</a>] Unlocking the Power of Large Language Models for Entity Alignment. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.14382" rel="nofollow">arxiv</a>] Enhancing Temporal Knowledge Graph Forecasting with Large Language Models via Chain-of-History Reasoning. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.13750" rel="nofollow">arxiv</a>] Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.13593" rel="nofollow">arxiv</a>] Knowledge Graph Enhanced Large Language Model Editing. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.12728" rel="nofollow">arxiv</a>] Modality-Aware Integration with Large Language Models for Knowledge-based Visual Question Answering. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.12352" rel="nofollow">arxiv</a>] Graph-Based Retriever Captures the Long Tail of Biomedical Knowledge. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.11804" rel="nofollow">arxiv</a>] LLM as Prompter: Low-resource Inductive Reasoning on Arbitrary Knowledge Graphs. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.11541" rel="nofollow">arxiv</a>] Counter-intuitive: Large Language Models Can Better Understand Knowledge Graphs Than We Thought. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.11441" rel="nofollow">arxiv</a>] InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.11323" rel="nofollow">arxiv</a>] Towards Development of Automated Knowledge Maps and Databases for Materials Engineering using Large Language Models. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.11163" rel="nofollow">arxiv</a>] KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.11034" rel="nofollow">arxiv</a>] PAT-Questions: A Self-Updating Benchmark for Present-Anchored Temporal Question-Answering. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.10779" rel="nofollow">arxiv</a>] A Condensed Transition Graph Framework for Zero-shot Link Prediction with Large Language Models. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.09911" rel="nofollow">arxiv</a>] Enhancing Large Language Models with Pseudo- and Multisource- Knowledge Graphs for Open-ended Question Answering. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.07630" rel="nofollow">arxiv</a>] G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.07148" rel="nofollow">arxiv</a>] X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Design. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.07016" rel="nofollow">arxiv</a>] REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.06764" rel="nofollow">arxiv</a>] GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encoding. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.05862" rel="nofollow">arxiv</a>] Let Your Graph Do the Talking: Encoding Structured Data for LLMs. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.05135" rel="nofollow">arxiv</a>] CADReN: Contextual Anchor-Driven Relational Network for Controllable Cross-Graphs Node Importance Estimation. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.04978" rel="nofollow">arxiv</a>] An Enhanced Prompt-Based LLM Reasoning Scheme via Knowledge Graph-Integrated Collaboration. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.04627" rel="nofollow">arxiv</a>] SPARQL Generation: an analysis on fine-tuning OpenLLaMA for Question Answering over a Life Science Knowledge Graph. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.03339" rel="nofollow">arxiv</a>] Interplay of Semantic Communication and Knowledge Learning. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.03299" rel="nofollow">arxiv</a>] GUARD: Role-playing to Generate Natural-language Jailbreakings to Test Guideline Adherence of Large Language Models. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.02130" rel="nofollow">arxiv</a>] Rendering Graphs for Graph Reasoning in Multimodal Large Language Models. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.01730" rel="nofollow">arxiv</a>] Evaluating LLM -- Generated Multimodal Diagnosis from Medical Images and Symptom Analysis. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.01729" rel="nofollow">EACL 2024</a>] Contextualization Distillation from Large Language Model for Knowledge Graph Completion. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.01495" rel="nofollow">EACL 2024</a>] A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2402.00414" rel="nofollow">arxiv</a>] Prompt-Time Symbolic Knowledge Capture with Large Language Models. <code>2024.02</code> [<a href="https://github.com/HaltiaAI/paper-PTSKC">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2402.00292" rel="nofollow">arxiv</a>] Effective Bug Detection in Graph Database Engines: An LLM-based Approach. <code>2024.02</code> </li> <li>[<a href="https://arxiv.org/abs/2401.16960" rel="nofollow">arxiv</a>] Two Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignment. <code>2024.01</code> </li> <li>[<a href="https://arxiv.org/abs/2401.14640" rel="nofollow">arxiv</a>] Benchmarking Large Language Models in Complex Question Answering Attribution using Knowledge Graphs. <code>2024.01</code> </li> <li>[<a href="https://arxiv.org/abs/2401.13444" rel="nofollow">arxiv</a>] Clue-Guided Path Exploration: An Efficient Knowledge Base Question-Answering Framework with Low Computational Resource Consumption. <code>2024.01</code> </li> <li>[<a href="https://arxiv.org/abs/2401.12863" rel="nofollow">AAAI 2024</a>] KAM-CoT: Knowledge Augmented Multimodal Chain-of-Thoughts Reasoning. <code>2024.01</code> </li> <li>[<a href="https://arxiv.org/abs/2401.12671" rel="nofollow">arxiv</a>] Context Matters: Pushing the Boundaries of Open-Ended Answer Generation with Graph-Structured Knowledge Context. <code>2024.01</code> </li> <li>[<a href="https://arxiv.org/abs/2401.08517" rel="nofollow">arxiv</a>] Supporting Student Decisions on Learning Recommendations: An LLM-Based Chatbot with Knowledge Graph Contextualization for Conversational Explainability and Mentoring. <code>2024.01</code> </li> <li>[<a href="https://arxiv.org/abs/2401.07237" rel="nofollow">arxiv</a>] Distilling Event Sequence Knowledge From Large Language Models. <code>2024.01</code> </li> <li>[<a href="https://arxiv.org/abs/2401.06853" rel="nofollow">ACL 24</a>] Large Language Models Can Learn Temporal Reasoning. <code>2024.01</code> [<a href="https://github.com/xiongsiheng/TG-LLM">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2401.06072" rel="nofollow">arxiv</a>] Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion. <code>2024.01</code> </li> <li>[<a href="https://arxiv.org/abs/2401.04507" rel="nofollow">arxiv</a>] TechGPT-2.0: A large language model project to solve the task of knowledge graph construction. <code>2024.01</code> [<a href="https://github.com/neukg/TechGPT-2.0">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2401.01711" rel="nofollow">arxiv</a>] Evaluating Large Language Models in Semantic Parsing for Conversational Question Answering over Knowledge Graphs. <code>2024.01</code> </li> <li>[<a href="https://arxiv.org/abs/2401.00761" rel="nofollow">arxiv</a>] The Earth is Flat? Unveiling Factual Errors in Large Language Models. <code>2024.01</code> </li> <li>[<a href="https://arxiv.org/abs/2401.00426" rel="nofollow">arxiv</a>] keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM. <code>2024.01</code> </li> <li>[<a href="https://arxiv.org/abs/2401.03158" rel="nofollow">arxiv</a>] Quartet Logic: A Four-Step Reasoning (QLFR) framework for advancing Short Text Classification. <code>2024.01</code> </li> <li>[<a href="https://arxiv.org/abs/2312.17269" rel="nofollow">arxiv</a>] Conversational Question Answering with Reformulations over Knowledge Graph. <code>2023.12</code> </li> <li>[<a href="https://arxiv.org/abs/2312.15883" rel="nofollow">arxiv</a>] Think and Retrieval: A Hypothesis Knowledge Graph Enhanced Medical Large Language Models. <code>2023.12</code> </li> <li>[<a href="https://arxiv.org/abs/2312.15880" rel="nofollow">arxiv</a>] KnowledgeNavigator: Leveraging Large Language Models for Enhanced Reasoning over Knowledge Graph. <code>2023.12</code> </li> <li>[<a href="https://arxiv.org/abs/2312.11813" rel="nofollow">arxiv</a>] Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment. <code>2023.12</code> </li> <li>[<a href="https://arxiv.org/abs/2312.11785" rel="nofollow">arxiv</a>] Zero-Shot Fact-Checking with Semantic Triples and Knowledge Graphs. <code>2023.12</code> </li> <li>[<a href="https://arxiv.org/abs/2312.11539" rel="nofollow">arxiv</a>] KGLens: A Parameterized Knowledge Graph Solution to Assess What an LLM Does and Doesn't Know. <code>2023.12</code> </li> <li>[<a href="https://arxiv.org/abs/2312.11282" rel="nofollow">arxiv</a>] LLM-ARK: Knowledge Graph Reasoning Using Large Language Models via Deep Reinforcement Learning. <code>2023.12</code> </li> <li>[<a href="https://arxiv.org/abs/2312.09126" rel="nofollow">arxiv</a>] Towards Trustworthy AI Software Development Assistance. <code>2023.12</code> </li> <li>[<a href="https://arxiv.org/abs/2312.06185" rel="nofollow">arxiv</a>] KnowGPT: Black-Box Knowledge Injection for Large Language Models. <code>2023.12</code> </li> <li>[<a href="https://arxiv.org/abs/2312.05276" rel="nofollow">arxiv</a>] Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation. <code>2023.12</code> </li> <li>[<a href="https://arxiv.org/abs/2312.03749" rel="nofollow">arxiv</a>] Conceptual Engineering Using Large Language Models. <code>2023.12</code> </li> <li>[<a href="https://arxiv.org/abs/2312.03022" rel="nofollow">arxiv</a>] Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Construction. <code>2023.12</code> </li> <li>[<a href="https://arxiv.org/abs/2312.01954" rel="nofollow">arxiv</a>] Zero- and Few-Shots Knowledge Graph Triplet Extraction with Large Language Models. <code>2023.12</code> </li> <li>[<a href="https://arxiv.org/abs/2312.00353" rel="nofollow">arxiv</a>] On Exploring the Reasoning Capability of Large Language Models with Knowledge Graphs. <code>2023.11</code> </li> <li>[<a href="https://arxiv.org/abs/2311.17330" rel="nofollow">arxiv</a>] Biomedical knowledge graph-optimized prompt generation for large language models. <code>2023.11</code> [<a href="https://github.com/BaranziniLab/KG_RAG">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2311.16137" rel="nofollow">arxiv</a>] A Graph-to-Text Approach to Knowledge-Grounded Response Generation in Human-Robot Interaction. <code>2023.11</code> </li> <li>[<a href="http://arxiv.org/abs/2311.01150" rel="nofollow">EMNLP 2023</a>]Revisiting the Knowledge Injection Frameworks. <code>2023.12</code> </li> <li>[<a href="https://aclanthology.org/2023.emnlp-main.143" rel="nofollow">EMNLP 2023</a>]Does the Correctness of Factual Knowledge Matter for Factual Knowledge-Enhanced Pre-trained Language Models? <code>2023.12</code> </li> <li>[<a href="https://aclanthology.org/2023.emnlp-main.228/" rel="nofollow">EMNLP 2023</a>]ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering over Knowledge Graph. <code>2023.12</code> </li> <li>[<a href="https://aclanthology.org/2023.findings-emnlp.580/" rel="nofollow">EMNLP 2023 Findings</a>]KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion. <code>2023.12</code> </li> <li>[<a href="https://arxiv.org/abs/2311.01862" rel="nofollow">arxiv</a>] <math-renderer class="js-inline-math" style="display: inline-block" data-static-url="https://github.githubassets.com/static" data-run-id="9b5c46fafee86632afb35d7dcf77cc09">$R^3$</math-renderer>-NL2GQL: A Hybrid Models Approach for for Accuracy Enhancing and Hallucinations Mitigation. <code>2023.11</code> </li> <li>[<a href="https://arxiv.org/abs/2311.13314" rel="nofollow">arxiv</a>] Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting. <code>2023.11</code> </li> <li>[<a href="https://arxiv.org/abs/2305.14202" rel="nofollow">EMNLP 2023</a>] Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata. <code>2023.11</code> </li> <li>[<a href="https://arxiv.org/abs/2311.09841" rel="nofollow">arxiv</a>] Leveraging LLMs in Scholarly Knowledge Graph Question Answering. <code>2023.11</code> </li> <li>[<a href="https://arxiv.org/abs/2311.06503" rel="nofollow">arxiv</a>] Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering. <code>2023.11</code> </li> <li>[<a href="https://arxiv.org/abs/2311.03837" rel="nofollow">arxiv</a>] OLaLa: Ontology Matching with Large Language Models. <code>2023.11</code> </li> <li>[<a href="https://arxiv.org/abs/2311.02956" rel="nofollow">arxiv</a>] In-Context Learning for Knowledge Base Question Answering for Unmanned Systems based on Large Language Models. <code>2023.11</code> </li> <li>[<a href="https://arxiv.org/abs/2311.01266" rel="nofollow">arxiv</a>] Let's Discover More API Relations: A Large Language Model-based AI Chain for Unsupervised API Relation Inference. <code>2023.11</code> </li> <li>[<a href="https://arxiv.org/abs/2311.00444" rel="nofollow">arxiv</a>] Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirements. <code>2023.11</code> </li> <li>[<a href="https://arxiv.org/abs/2310.02166" rel="nofollow">arxiv</a>] Large Language Models Meet Knowledge Graphs to Answer Factoid Questions. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.07008" rel="nofollow">arxiv</a>] Answer Candidate Type Selection: Text-to-Text Language Model for Closed Book Question Answering Meets Knowledge Graphs. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2311.00287" rel="nofollow">arxiv</a>] Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.20170" rel="nofollow">arxiv</a>] DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.19998" rel="nofollow">arxiv</a>] Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.18951" rel="nofollow">arxiv</a>] A Multimodal Ecological Civilization Pattern Recommendation Method Based on Large Language Models and Knowledge Graph. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.18356" rel="nofollow">arxiv</a>] LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.16421" rel="nofollow">arxiv</a>] Graph Agent: Explicit Reasoning Agent for Graphs. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.14174" rel="nofollow">arxiv</a>] An In-Context Schema Understanding Method for Knowledge Base Question Answering. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.13023" rel="nofollow">arxiv</a>] GraphGPT: Graph Instruction Tuning for Large Language Models. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.11638" rel="nofollow">EMNLP 2023 Findings</a>] Systematic Assessment of Factual Knowledge in Large Language Models. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.11220" rel="nofollow">EMNLP 2023 Findings</a>] KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.10445" rel="nofollow">arxiv</a>] MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalities. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.09089" rel="nofollow">arxiv</a>] Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.08975" rel="nofollow">arxiv</a>] ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.08365" rel="nofollow">arxiv</a>] From Large Language Models to Knowledge Graphs for Biomarker Discovery in Cancer. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.06671" rel="nofollow">arxiv</a>] Making Large Language Models Perform Better in Knowledge Graph Completion. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.08279" rel="nofollow">arxiv</a>] CP-KGC: Constrained-Prompt Knowledge Graph Completion with Large Language Models. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.07170" rel="nofollow">arxiv</a>] PHALM: Building a Knowledge Graph from Scratch by Prompting Humans and a Language Model. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.03269" rel="nofollow">arxiv</a>] InstructProtein: Aligning Human and Protein Language via Knowledge Instruction. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.01290" rel="nofollow">arxiv</a>] Knowledge Crosswords: Geometric Reasoning over Structured Knowledge with Large Language Models. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2310.01061" rel="nofollow">ICLR 2024</a>] Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning. <code>2023.10</code> [<a href="https://github.com/RManLuo/reasoning-on-graphs">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2310.00299" rel="nofollow">arxiv</a>] RelBERT: Embedding Relations with Language Models. <code>2023.10</code> </li> <li>[<a href="https://arxiv.org/abs/2309.17122" rel="nofollow">arxiv</a>] Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?. <code>2023.09</code> </li> <li>[<a href="https://arxiv.org/pdf/2309.16134" rel="nofollow">arxiv</a>] Let's Chat to Find the APIs: Connecting Human, LLM and Knowledge Graph through AI Chain. <code>2023.09</code> </li> <li>[<a href="https://arxiv.org/pdf/2309.15427" rel="nofollow">arxiv</a>] Graph Neural Prompting with Large Language Models. <code>2023.09</code> </li> <li>[<a href="https://arxiv.org/abs/2309.12132" rel="nofollow">arxiv</a>] A knowledge representation approach for construction contract knowledge modeling. <code>2023.09</code> </li> <li>[<a href="https://arxiv.org/abs/2309.11206" rel="nofollow">arxiv</a>] Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering. <code>2023.09</code> </li> <li>[<a href="https://arxiv.org/abs/2309.08594" rel="nofollow">arxiv</a>] "Merge Conflicts!" Exploring the Impacts of External Distractors to Parametric Knowledge Graphs. <code>2023.09</code> </li> <li>[<a href="https://arxiv.org/abs/2309.00240" rel="nofollow">arxiv</a>] FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking. <code>2023.09</code> </li> <li>[<a href="https://arxiv.org/pdf/2309.01538" rel="nofollow">arxiv</a>] ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning. <code>2023.09</code> </li> <li>[<a href="https://arxiv.org/abs/2309.04695" rel="nofollow">AAAI 2024</a>] Code-Style In-Context Learning for Knowledge-Based Question Answering. <code>2023.09</code> </li> <li>[<a href="https://arxiv.org/abs/2309.04565" rel="nofollow">arxiv</a>] Unleashing the Power of Graph Learning through LLM-based Autonomous Agents. <code>2023.09</code> </li> <li>[<a href="https://arxiv.org/abs/2309.04175" rel="nofollow">arxiv</a>] Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Reliable Response Generation in Chinese. <code>2023.09</code> </li> <li>[<a href="https://arxiv.org/abs/2309.03118" rel="nofollow">arxiv</a>] Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from Knowledge Graphs. <code>2023.09</code> </li> <li>[<a href="https://arxiv.org/abs/2308.14429" rel="nofollow">arxiv</a>] Biomedical Entity Linking with Triple-aware Pre-Training. <code>2023.08</code> </li> <li>[<a href="https://arxiv.org/abs/2308.13916" rel="nofollow">arxiv</a>] Exploring Large Language Models for Knowledge Graph Completion. <code>2023.08</code> [<a href="https://github.com/yao8839836/kg-llm">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2308.16622" rel="nofollow">arxiv</a>] Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering. <code>2023.08</code> </li> <li>[<a href="https://arxiv.org/abs/2308.14321" rel="nofollow">arxiv</a>] Leveraging A Medical Knowledge Graph into Large Language Models for Diagnosis Prediction. <code>2023.08</code> </li> <li>[<a href="https://arxiv.org/abs/2308.12028" rel="nofollow">arxiv</a>] LKPNR: LLM and KG for Personalized News Recommendation Framework. <code>2023.08</code> </li> <li>[<a href="https://arxiv.org/abs/2308.11730" rel="nofollow">arxiv</a>] Knowledge Graph Prompting for Multi-Document Question Answering. <code>2023.08</code> </li> <li>[<a href="https://arxiv.org/abs/2308.10168" rel="nofollow">arxiv</a>] Head-to-Tail: How Knowledgeable are Large Language Models (LLM)? A.K.A. Will LLMs Replace Knowledge Graphs?. <code>2023.08</code> </li> <li>[<a href="https://arxiv.org/abs/2308.09729" rel="nofollow">arxiv</a>] MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models. <code>2023.08</code> </li> <li>[<a href="https://arxiv.org/abs/2308.00081" rel="nofollow">arxiv</a>] Towards Semantically Enriched Embeddings for Knowledge Graph Completion. <code>2023.07</code> </li> <li>[<a href="https://arxiv.org/abs/2307.11772" rel="nofollow">TKDE 2024</a>] AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models. <code>2023.07</code> </li> <li>[<a href="https://arxiv.org/abs/2307.07312" rel="nofollow">arxiv</a>] Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge Graphs. <code>2023.07</code> </li> <li>[<a href="https://arxiv.org/abs/2307.07697" rel="nofollow">ICLR 2024</a>] Think-on-Graph: Deep and Responsible Reasoning of Large Language Model with Knowledge Graph. <code>2023.07</code> </li> <li>[<a href="https://arxiv.org/abs/2307.03393" rel="nofollow">SIGKDD 2024 Explorations</a>] Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs. <code>2023.07</code> </li> <li>[<a href="https://arxiv.org/abs/2307.05722" rel="nofollow">arxiv</a>] Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations. <code>2023.07</code> </li> <li>[<a href="https://arxiv.org/abs/2307.02738" rel="nofollow">arxiv</a>] RecallM: An Architecture for Temporal Context Understanding and Question Answering. <code>2023.07</code> </li> <li>[<a href="https://arxiv.org/abs/2307.06917" rel="nofollow">arxiv</a>] LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT. <code>2023.07</code> </li> <li>[<a href="https://arxiv.org/abs/2307.01128" rel="nofollow">arxiv</a>] Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction. <code>2023.07</code> </li> <li>[<a href="https://arxiv.org/abs/2306.10241" rel="nofollow">arxiv</a>] Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model . <code>2023.06</code> </li> <li>[<a href="https://arxiv.org/pdf/2306.04136.pdf" rel="nofollow">arxiv</a>] Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering. <code>2023.06</code> </li> <li>[<a href="https://arxiv.org/abs/2306.10723" rel="nofollow">arxiv</a>] Fine-tuning Large Enterprise Language Models via Ontological Reasoning. <code>2023.06</code> </li> <li>[<a href="https://arxiv.org/abs/2305.18395" rel="nofollow">NeurIPS 2023</a>] Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks. <code>2023.05</code> </li> <li>[<a href="https://arxiv.org/pdf/2305.04676" rel="nofollow">arxiv</a>] Enhancing Knowledge Graph Construction Using Large Language Models. <code>2023.05</code> </li> <li>[<a href="https://arxiv.org/abs/2305.03513" rel="nofollow">arxiv</a>] ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs. <code>2023.05</code> </li> <li>[<a href="https://arxiv.org/abs/2305.06590" rel="nofollow">ACL 2023</a>] FactKG: Fact Verification via Reasoning on Knowledge Graphs. <code>2023.05</code> </li> <li>[<a href="https://arxiv.org/abs/2304.05973" rel="nofollow">arxiv</a>] HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting. <code>2023.04</code> </li> <li>[<a href="https://arxiv.org/abs/2305.09645" rel="nofollow">EMNLP 2023</a>] StructGPT: A General Framework for Large Language Model to Reason over Structured Data. <code>2023.05</code> </li> <li>[<a href="https://arxiv.org/abs/2305.19523" rel="nofollow">ICLR 2024</a>] Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning. <code>2023.05</code> </li> <li>[<a href="https://arxiv.org/abs/2305.13168" rel="nofollow">arxiv</a>] LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities. <code>2023.05</code> [<a href="https://github.com/zjunlp/AutoKG">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2305.10037" rel="nofollow">NeurIPS 2023</a>] Can Language Models Solve Graph Problems in Natural Language? <code>2023.05</code> </li> <li>[<a href="https://arxiv.org/abs/2305.09858" rel="nofollow">arxiv</a>] Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs. <code>2023.05</code> </li> <li>[<a href="https://arxiv.org/abs/2305.16755" rel="nofollow">arxiv</a>] Can large language models generate salient negative statements? <code>2023.05</code> </li> <li>[<a href="https://arxiv.org/abs/2305.15066" rel="nofollow">arxiv</a>] GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking. <code>2023.05</code> </li> <li>[<a href="https://arxiv.org/abs/2305.01157" rel="nofollow">arxiv</a>] Complex Logical Reasoning over Knowledge Graphs using Large Language Models. <code>2023.05</code> [<a href="https://github.com/Akirato/LLM-KG-Reasoning/tree/main">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2305.00050" rel="nofollow">arxiv</a>] Causal Reasoning and Large Language Models: Opening a New Frontier for Causality. <code>2023.04</code> </li> <li>[<a href="https://arxiv.org/abs/2303.05279" rel="nofollow">arxiv</a>] Can large language models build causal graphs? <code>2023.04</code> </li> <li>[<a href="https://arxiv.org/abs/2304.05774" rel="nofollow">arxiv</a>] Using Multiple RDF Knowledge Graphs for Enriching ChatGPT Responses. <code>2023.04</code> </li> <li>[<a href="https://arxiv.org/abs/2304.11116" rel="nofollow">arxiv</a>] Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT. <code>2023.04</code> </li> <li>[<a href="https://arxiv.org/abs/2304.02711" rel="nofollow">arxiv</a>] Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning. <code>2023.04</code> [<a href="https://github.com/monarch-initiative/ontogpt">Repo</a>]</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Resources and Benchmarking</h3><a id="user-content-resources-and-benchmarking" class="anchor" aria-label="Permalink: Resources and Benchmarking" href="#resources-and-benchmarking"><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="https://arxiv.org/abs/2404.13207" rel="nofollow">arxiv</a>] STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases. <code>2024.04</code> [<a href="https://github.com/snap-stanford/stark">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2402.06341" rel="nofollow">arxiv</a>] RareBench: Can LLMs Serve as Rare Diseases Specialists?. <code>2024.02</code></li> <li>[<a href="https://arxiv.org/abs/2401.14640" rel="nofollow">arxiv</a>] Benchmarking Large Language Models in Complex Question Answering Attribution using Knowledge Graphs. <code>2024.01</code></li> <li>[<a href="https://arxiv.org/abs/2401.06853" rel="nofollow">ACL 24</a>] Large Language Models Can Learn Temporal Reasoning. <code>2024.01</code> [<a href="https://github.com/xiongsiheng/TG-LLM">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2311.09174" rel="nofollow">arxiv</a>] AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph. <code>2023.11</code></li> <li>[<a href="https://arxiv.org/abs/2311.07509" rel="nofollow">arxiv</a>] A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases. <code>2023.11</code></li> <li>[<a href="https://arxiv.org/abs/2310.05634" rel="nofollow">arxiv</a>] Towards Verifiable Generation: A Benchmark for Knowledge-aware Language Model Attribution. <code>2023.10</code></li> <li>[<a href="https://arxiv.org/abs/2310.15517" rel="nofollow">EMNLP 2023</a>] MarkQA: A large scale KBQA dataset with numerical reasoning. <code>2023.10</code></li> <li>[<a href="https://arxiv.org/abs/2306.14704" rel="nofollow">CIKM 2023</a>] Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement. <code>2023.06</code></li> <li>[<a href="https://arxiv.org/abs/2306.05783" rel="nofollow">arxiv</a>] Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation. <code>2023.06</code></li> <li>[<a href="https://arxiv.org/abs/2210.00305" rel="nofollow">AACL 2023 System Demonstrations</a>] LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings <code>2023.03</code> [<a href="http://47.92.96.190:9001/" rel="nofollow">Repo</a>]</li> <li>[<a href="https://arxiv.org/abs/2309.11669" rel="nofollow">arxiv</a>] Construction of Paired Knowledge Graph-Text Datasets Informed by Cyclic Evaluation. <code>2023.09</code></li> <li>[<a href="https://arxiv.org/abs/2310.08365" rel="nofollow">arxiv</a>] From Large Language Models to Knowledge Graphs for Biomarker Discovery in Cancer. <code>2023.10</code></li> <li>[<a href="https://arxiv.org/abs/2308.02357" rel="nofollow">ISWC 2023</a>] Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text. <code>2023.08</code></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Contribution</h2><a id="user-content-contribution" class="anchor" aria-label="Permalink: Contribution" href="#contribution"><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">👥 Contributors</h3><a id="user-content--contributors" class="anchor" aria-label="Permalink: 👥 Contributors" href="#-contributors"><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> <a href="https://github.com/zjukg/KG-LLM-Papers/graphs/contributors"> <img src="https://camo.githubusercontent.com/2587b3dcfd9b268a898efd413cf2fe89756c0600753691bd764a9e1402499694/68747470733a2f2f636f6e747269622e726f636b732f696d6167653f7265706f3d7a6a756b672f4b472d4c4c4d2d506170657273" data-canonical-src="https://contrib.rocks/image?repo=zjukg/KG-LLM-Papers" style="max-width: 100%;"> </a> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">🎉 Contributing ( welcome ! )</h3><a id="user-content--contributing--welcome--" class="anchor" aria-label="Permalink: 🎉 Contributing ( welcome ! )" href="#-contributing--welcome--"><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>✨ Add a new paper or update an existing KG-related LLM paper.</li> <li>🧐 Use the same format as existing entries to describe the work.</li> <li>😄 A very brief explanation why you think a paper should be added or updated is recommended (Not Neccessary) via <strong><code>Adding Issues</code></strong> or <strong><code>Pull Requests</code></strong>.</li> </ul> <p dir="auto"><strong>Don't worry if you put something wrong, they will be fixed for you. Just feel free to contribute and promote your awesome work here! 🤩 We'll get back to you in time ~ 😉</strong></p> <p dir="auto"><a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/a63dd8f593a6536ad4b7ce5978d689ef71596be697885c81d62da490d2eb87cd/68747470733a2f2f6170692e737461722d686973746f72792e636f6d2f7376673f7265706f733d7a6a756b672f4b472d4c4c4d2d50617065727326747970653d44617465"><img src="https://camo.githubusercontent.com/a63dd8f593a6536ad4b7ce5978d689ef71596be697885c81d62da490d2eb87cd/68747470733a2f2f6170692e737461722d686973746f72792e636f6d2f7376673f7265706f733d7a6a756b672f4b472d4c4c4d2d50617065727326747970653d44617465" alt="Star History Chart" data-canonical-src="https://api.star-history.com/svg?repos=zjukg/KG-LLM-Papers&type=Date" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">🤝 Cite:</h2><a id="user-content--cite" class="anchor" aria-label="Permalink: 🤝 Cite:" href="#-cite"><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">If this Repo is helpful to you, please consider citing one of our papers. We would greatly appreciate it :)</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@inproceedings{DBLP:conf/nips/GuoB000LSZLLZZC24, author = {Lingbing Guo and Zhongpu Bo and Zhuo Chen and Yichi Zhang and Jiaoyan Chen and Yarong Lan and Mengshu Sun and Zhiqiang Zhang and Yangyifei Luo and Qian Li and Qiang Zhang and Wen Zhang and Huajun Chen}, title = {{MKGL:} Mastery of a Three-Word Language}, booktitle = {NeurIPS}, year = {2024} }"><pre lang="bigquery" class="notranslate"><code>@inproceedings{DBLP:conf/nips/GuoB000LSZLLZZC24, author = {Lingbing Guo and Zhongpu Bo and Zhuo Chen and Yichi Zhang and Jiaoyan Chen and Yarong Lan and Mengshu Sun and Zhiqiang Zhang and Yangyifei Luo and Qian Li and Qiang Zhang and Wen Zhang and Huajun Chen}, title = {{MKGL:} Mastery of a Three-Word Language}, booktitle = {NeurIPS}, year = {2024} } </code></pre></div> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@inproceedings{DBLP:conf/acl/ZhangCFLL0C24, author = {Yichi Zhang and Zhuo Chen and Yin Fang and Yanxi Lu and Fangming Li and Wen Zhang and Huajun Chen}, title = {Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering}, booktitle = {{ACL} (Findings)}, pages = {891--904}, publisher = {Association for Computational Linguistics}, year = {2024} } "><pre lang="bigquery" class="notranslate"><code>@inproceedings{DBLP:conf/acl/ZhangCFLL0C24, author = {Yichi Zhang and Zhuo Chen and Yin Fang and Yanxi Lu and Fangming Li and Wen Zhang and Huajun Chen}, title = {Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering}, booktitle = {{ACL} (Findings)}, pages = {891--904}, publisher = {Association for Computational Linguistics}, year = {2024} } </code></pre></div> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@inproceedings{DBLP:conf/mm/00090GX0C24, author = {Yichi Zhang and Zhuo Chen and Lingbing Guo and Yajing Xu and Wen Zhang and Huajun Chen}, title = {Making Large Language Models Perform Better in Knowledge Graph Completion}, booktitle = {{ACM} Multimedia}, pages = {233--242}, publisher = {{ACM}}, year = {2024} }"><pre lang="bigquery" class="notranslate"><code>@inproceedings{DBLP:conf/mm/00090GX0C24, author = {Yichi Zhang and Zhuo Chen and Lingbing Guo and Yajing Xu and Wen Zhang and Huajun Chen}, title = {Making Large Language Models Perform Better in Knowledge Graph Completion}, booktitle = {{ACM} Multimedia}, pages = {233--242}, publisher = {{ACM}}, year = {2024} } </code></pre></div> </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="hqwSeCx6qbIeS/Ui9/KdBeCVpDzklbKjNfQ6AERtB068HiPEUQj/N59Am8U4TuRJPRf/JXSwUBqMJ+csgrSmqA==" /> </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"> [Paper List] Papers integrating knowledge graphs (KGs) and large language models (LLMs) </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/nlp" title="Topic: nlp" data-view-component="true" class="topic-tag topic-tag-link"> nlp </a> <a href="/topics/awesome" title="Topic: awesome" data-view-component="true" class="topic-tag topic-tag-link"> awesome </a> <a href="/topics/knowledge" title="Topic: knowledge" data-view-component="true" class="topic-tag topic-tag-link"> knowledge </a> <a href="/topics/prompt" title="Topic: prompt" data-view-component="true" class="topic-tag topic-tag-link"> prompt </a> <a href="/topics/survey" title="Topic: survey" data-view-component="true" class="topic-tag topic-tag-link"> survey </a> <a href="/topics/knowledge-graph" title="Topic: knowledge-graph" data-view-component="true" class="topic-tag topic-tag-link"> knowledge-graph </a> <a href="/topics/gpt" title="Topic: gpt" data-view-component="true" class="topic-tag topic-tag-link"> gpt </a> <a href="/topics/language-models" title="Topic: language-models" data-view-component="true" class="topic-tag topic-tag-link"> language-models </a> <a href="/topics/commonsense" title="Topic: commonsense" data-view-component="true" class="topic-tag topic-tag-link"> commonsense </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/large-language-models" title="Topic: large-language-models" 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