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GitHub - allenai/safety-eval: A simple evaluation of generative language models and safety classifiers.
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0 0 1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 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" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon 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data-target="react-partial.embeddedData">{"props":{"initialPayload":{"allShortcutsEnabled":false,"path":"/","repo":{"id":817689591,"defaultBranch":"main","name":"safety-eval","ownerLogin":"allenai","currentUserCanPush":false,"isFork":false,"isEmpty":false,"createdAt":"2024-06-20T08:37:14.000Z","ownerAvatar":"https://avatars.githubusercontent.com/u/5667695?v=4","public":true,"private":false,"isOrgOwned":true},"currentUser":null,"refInfo":{"name":"main","listCacheKey":"v0:1719907033.0","canEdit":false,"refType":"branch","currentOid":"a173993e09a9cce0086522d864c3cba07de31ced"},"tree":{"items":[{"name":"evaluation","path":"evaluation","contentType":"directory"},{"name":"src","path":"src","contentType":"directory"},{"name":"LICENSE.md","path":"LICENSE.md","contentType":"file"},{"name":"README.md","path":"README.md","contentType":"file"},{"name":"requirements.txt","path":"requirements.txt","contentType":"file"},{"name":"setup.py","path":"setup.py","contentType":"file"}],"templateDirectorySuggestionUrl":null,"readme":null,"totalCount":6,"showBranchInfobar":false},"fileTree":null,"fileTreeProcessingTime":null,"foldersToFetch":[],"treeExpanded":false,"symbolsExpanded":false,"isOverview":true,"overview":{"banners":{"shouldRecommendReadme":false,"isPersonalRepo":false,"showUseActionBanner":false,"actionSlug":null,"actionId":null,"showProtectBranchBanner":false,"publishBannersInfo":{"dismissActionNoticePath":"/settings/dismiss-notice/publish_action_from_repo","releasePath":"/allenai/safety-eval/releases/new?marketplace=true","showPublishActionBanner":false},"interactionLimitBanner":null,"showInvitationBanner":false,"inviterName":null,"actionsMigrationBannerInfo":{"releaseTags":[],"showImmutableActionsMigrationBanner":false,"initialMigrationStatus":null}},"codeButton":{"contactPath":"/contact","isEnterprise":false,"local":{"protocolInfo":{"httpAvailable":true,"sshAvailable":null,"httpUrl":"https://github.com/allenai/safety-eval.git","showCloneWarning":null,"sshUrl":null,"sshCertificatesRequired":null,"sshCertificatesAvailable":null,"ghCliUrl":"gh 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Tool 🧰 (Evaluation Suite)\u003c/h1\u003e\u003ca id=\"user-content-ai2-safety-tool--evaluation-suite\" class=\"anchor\" aria-label=\"Permalink: Ai2 Safety Tool 🧰 (Evaluation Suite)\" href=\"#ai2-safety-tool--evaluation-suite\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis repository contains code for easy and comprehensive safety evaluation on generative LMs and safety moderation tools. This evaluation framework is used in safety projects at Ai2, including:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eWildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2406.18510\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d2fef28a9e196cee4529bddd6443ede19de6e45c64743c2f702540af81a8c398/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09f939d2d70617065722d626c7565\" data-canonical-src=\"https://img.shields.io/badge/📝-paper-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/allenai/wildteaming\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4da4d8b419c5146973309c3fd5e5660e50a3f6382be8fd286b3bb6976058d44a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09f94972d636f64652d726564\" data-canonical-src=\"https://img.shields.io/badge/🔗-code-red\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://huggingface.co/allenai/llama2-7b-WildJailbreak\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9ac3e3d2b807c98bfb4b7c9bd4a389581f1f86840ec18d9863bd41b85544f824/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09fa4972d74756c75322d2d37622d2d77696c646a61696c627265616b20286d6f64656c292d677265656e\" data-canonical-src=\"https://img.shields.io/badge/🤗-tulu2--7b--wildjailbreak (model)-green\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://huggingface.co/allenai/llama2-13b-WildJailbreak\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/763a4849a8855a35fcba558d634d229e436a9ec16850b444322e4b8d3dfa4e81/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09fa4972d74756c75322d2d3133622d2d77696c646a61696c627265616b20286d6f64656c292d677265656e\" data-canonical-src=\"https://img.shields.io/badge/🤗-tulu2--13b--wildjailbreak (model)-green\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://huggingface.co/datasets/allenai/wildjailbreak\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/ea8d4e2cbf16a53ea8d9dc34f0c4ec2fa7a6f13f0d9dc436b4db34a023232aed/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09fa4972d77696c646a61696c627265616b202864617461292d6f72616e6765\" data-canonical-src=\"https://img.shields.io/badge/🤗-wildjailbreak (data)-orange\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eWildGuard: Open One-stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2406.18495\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d2fef28a9e196cee4529bddd6443ede19de6e45c64743c2f702540af81a8c398/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09f939d2d70617065722d626c7565\" data-canonical-src=\"https://img.shields.io/badge/📝-paper-blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/allenai/wildguard\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4da4d8b419c5146973309c3fd5e5660e50a3f6382be8fd286b3bb6976058d44a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09f94972d636f64652d726564\" data-canonical-src=\"https://img.shields.io/badge/🔗-code-red\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://huggingface.co/allenai/wildguard\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6a3fa1c7b0e2148534b8c5f2086c177169387bfdf24da4380dc17fa980fd828c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09fa4972d77696c64677561726420286d6f64656c292d677265656e\" data-canonical-src=\"https://img.shields.io/badge/🤗-wildguard (model)-green\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://huggingface.co/datasets/allenai/wildguardmix\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/76c1967129423e4635ab79998d22df225f8d69f2ad99e0a39246859adea6a606/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09fa4972d77696c6467756172646d6978202864617461292d6f72616e6765\" data-canonical-src=\"https://img.shields.io/badge/🤗-wildguardmix (data)-orange\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eThis lets you to display how the performance changes across more than 14 benchmarks as below 🔥\u003c/p\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://private-user-images.githubusercontent.com/162524131/350415777-7d29f65f-ab6a-4164-8adc-0c1dc860bb30.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MzIzNTgxMDksIm5iZiI6MTczMjM1NzgwOSwicGF0aCI6Ii8xNjI1MjQxMzEvMzUwNDE1Nzc3LTdkMjlmNjVmLWFiNmEtNDE2NC04YWRjLTBjMWRjODYwYmIzMC5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQxMTIzJTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MTEyM1QxMDMwMDlaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT0xZTdhYWRmN2U0ZjdkM2U5MTE5YjllYjBiNDZjM2E4ZjUzOGE1ZTM5ZDViOWY0NTg1MDBhYzVlZDU5NDFkNTdlJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCJ9.oVxHwCKkbT6Hu5HF2HjY9wIgRLk-gyhxeLW1yCZW-78\"\u003e\u003cimg width=\"610\" alt=\"image\" src=\"https://private-user-images.githubusercontent.com/162524131/350415777-7d29f65f-ab6a-4164-8adc-0c1dc860bb30.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MzIzNTgxMDksIm5iZiI6MTczMjM1NzgwOSwicGF0aCI6Ii8xNjI1MjQxMzEvMzUwNDE1Nzc3LTdkMjlmNjVmLWFiNmEtNDE2NC04YWRjLTBjMWRjODYwYmIzMC5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQxMTIzJTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MTEyM1QxMDMwMDlaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT0xZTdhYWRmN2U0ZjdkM2U5MTE5YjllYjBiNDZjM2E4ZjUzOGE1ZTM5ZDViOWY0NTg1MDBhYzVlZDU5NDFkNTdlJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCJ9.oVxHwCKkbT6Hu5HF2HjY9wIgRLk-gyhxeLW1yCZW-78\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUpdates\u003c/h2\u003e\u003ca id=\"user-content-updates\" class=\"anchor\" aria-label=\"Permalink: Updates\" href=\"#updates\"\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\u003e2024-07-06: add support for MMLU, TruthfulQA, and sorrybench classifier.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\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\u003eEasy evaluation of \u003cstrong\u003egenerative language models\u003c/strong\u003e on list of \u003cem\u003esafety\u003c/em\u003e benchmarks and \u003cem\u003egeneral\u003c/em\u003e capabilities benchmarks such as MT-Bench and AlpacaEval2.\u003c/li\u003e\n\u003cli\u003eEasy evaluation of \u003cstrong\u003esafety classifiers\u003c/strong\u003e on three tasks (detection of \u003cem\u003eprompt harmfulness\u003c/em\u003e, \u003cem\u003eresponse harmfulness\u003c/em\u003e, and \u003cem\u003eresponse refusal\u003c/em\u003e) across 10+ benchmarks.\u003c/li\u003e\n\u003cli\u003eEasy addition of new benchmarks and models to the evaluation suite.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\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=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"conda create -n safety-eval python=3.10 \u0026amp;\u0026amp; conda activate safety-eval\npip install -e .\npip install -r requirements.txt\npip install vllm==0.4.2\"\u003e\u003cpre\u003econda create -n safety-eval python=3.10 \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e conda activate safety-eval\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\npip install -r requirements.txt\npip install vllm==0.4.2\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003cem\u003eGenerative\u003c/em\u003e LM Evaluation\u003c/h2\u003e\u003ca id=\"user-content-generative-lm-evaluation\" class=\"anchor\" aria-label=\"Permalink: Generative LM Evaluation\" href=\"#generative-lm-evaluation\"\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\"\u003eSafety benchmarks\u003c/h3\u003e\u003ca id=\"user-content-safety-benchmarks\" class=\"anchor\" aria-label=\"Permalink: Safety benchmarks\" href=\"#safety-benchmarks\"\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\"\u003eFor all benchmarks requiring safety classification unless noted otherwise, as a default, we use the \u003ca href=\"https://github.com/allenai/wildguard\"\u003eWildGuard\u003c/a\u003e classifier to evaluate the safety of model outputs.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2406.18495\" rel=\"nofollow\"\u003eWildGuardTest\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2402.04249\" rel=\"nofollow\"\u003eHarmbench\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2203.09509\" rel=\"nofollow\"\u003eToxiGen\u003c/a\u003e: use \u003ccode\u003etomh/toxigen_roberta\u003c/code\u003e as the classifier\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2308.01263\" rel=\"nofollow\"\u003eXSTest\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2401.05561\" rel=\"nofollow\"\u003eJailbreakTrigger (in TrustLLM)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2308.03825\" rel=\"nofollow\"\u003eDo-anything-now\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2406.18510\" rel=\"nofollow\"\u003eWildJailbreak\u003c/a\u003e (both harmful and benign contrast sets)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eChanging classifiers for safety benchmarks\u003c/strong\u003e:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou can change the safety classifier used for evaluation by specifying the \u003ccode\u003eclassifier_model_name\u003c/code\u003e in the yaml file.\nFor example, when you want to use the HarmBench's classifiers for evaluation on HarmBench, you can use \u003ccode\u003eHarmbenchClassifier\u003c/code\u003e as the \u003ccode\u003eclassifier_model_name\u003c/code\u003e. Please check out the \u003ccode\u003eevaluation/tasks/generation/harmbench/default.yaml\u003c/code\u003e and \u003ccode\u003eevaluation/tasks/classification/harmbench/harmbench_classsifier.yaml\u003c/code\u003e to see the classifier's specification.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# evaluation/tasks/classification/harmbench/harmbench_classsifier.yaml\ntask_class: HarmbenchVanilla\nclassifier_model_name: HarmbenchClassifier\n\n# evaluation/tasks/generation/harmbench/default.yaml\ntask_class: HarmbenchVanilla\nclassifier_model_name: WildGuard\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e evaluation/tasks/classification/harmbench/harmbench_classsifier.yaml\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003etask_class\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eHarmbenchVanilla\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eclassifier_model_name\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eHarmbenchClassifier\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e evaluation/tasks/generation/harmbench/default.yaml\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003etask_class\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eHarmbenchVanilla\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003eclassifier_model_name\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eWildGuard\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePlease refer to \u003ccode\u003esrc/classifier_models/\u003c/code\u003e directory to explore the classifiers implementation.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGeneral capabilities benchmarks\u003c/h3\u003e\u003ca id=\"user-content-general-capabilities-benchmarks\" class=\"anchor\" aria-label=\"Permalink: General capabilities benchmarks\" href=\"#general-capabilities-benchmarks\"\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\"\u003eOptimal safety training maintains or even improves models' general capabilities. We include general capability evaluation for monitoring this dimension of safety training.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2404.04475\" rel=\"nofollow\"\u003eAlpacaEval (V2)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2306.05685\" rel=\"nofollow\"\u003eMTBench\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2110.14168\" rel=\"nofollow\"\u003eGSM8K\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2210.09261\" rel=\"nofollow\"\u003eBig Bench Hard (BBH)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2107.03374\" rel=\"nofollow\"\u003eCodex-Eval\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2009.03300\" rel=\"nofollow\"\u003eMMLU\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2109.07958\" rel=\"nofollow\"\u003eTruthfulQA\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eSupport for additional benchmarks, including \u003ca href=\"https://arxiv.org/abs/2311.07911\" rel=\"nofollow\"\u003eIFEval\u003c/a\u003e, and \u003ca href=\"https://arxiv.org/abs/2003.05002\" rel=\"nofollow\"\u003eTyDiQA\u003c/a\u003e is in progress.\nFor TydiQA, please use \u003ca href=\"https://github.com/allenai/open-instruct\"\u003eopen-instruct\u003c/a\u003e to evaluate the models for now.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow-to-use\u003c/h3\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How-to-use\" href=\"#how-to-use\"\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\"\u003eBelow are commands to run safety and general capability benchmarking for generative LMs. The first command can be used to run all included benchmarks for models which support vLLM. The second command can be used to select individual benchmarks for evaluation.\nTo specify a task, the syntax is \u003ccode\u003e\u0026lt;folder\u0026gt;:\u0026lt;config_yaml\u0026gt;\u003c/code\u003e, where \u003ccode\u003efolder\u003c/code\u003e is a folder under \u003ccode\u003etasks/generation\u003c/code\u003e and \u003ccode\u003econfig_yaml\u003c/code\u003e is the name of the configuration yaml file excluding \u003ccode\u003e.yaml\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# run all generation benchmarks by a single command. assume you are using vllm. \n# note that you should add OPENAI_API_KEY to your environment variables when you use mtbench and alpacaeval2.\nexport CUDA_VISIBLE_DEVICES={NUM_GPUS};\npython evaluation/run_all_generation_benchmarks.py \\\n --model_name_or_path allenai/tulu-2-dpo-7b \\\n --model_input_template_path_or_name tulu2 \\\n --report_output_path ./generation_results/metrics.json \\\n --save_individual_results_path ./generation_results/all.json\n \n# run specific generation benchmarks by a single command. here, we use three benchmarks.\npython evaluation/eval.py generators \\\n --use_vllm \\\n --model_name_or_path allenai/tulu-2-dpo-7b \\\n --model_input_template_path_or_name tulu2 \\\n --tasks wildguardtest,harmbench,toxigen:tiny \\\n --report_output_path ./generation_results/metrics.json \\\n --save_individual_results_path ./generation_results/all.json\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e run all generation benchmarks by a single command. assume you are using vllm. \u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e note that you should add OPENAI_API_KEY to your environment variables when you use mtbench and alpacaeval2.\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CUDA_VISIBLE_DEVICES={NUM_GPUS}\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\npython evaluation/run_all_generation_benchmarks.py \\\n --model_name_or_path allenai/tulu-2-dpo-7b \\\n --model_input_template_path_or_name tulu2 \\\n --report_output_path ./generation_results/metrics.json \\\n --save_individual_results_path ./generation_results/all.json\n \n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e run specific generation benchmarks by a single command. here, we use three benchmarks.\u003c/span\u003e\npython evaluation/eval.py generators \\\n --use_vllm \\\n --model_name_or_path allenai/tulu-2-dpo-7b \\\n --model_input_template_path_or_name tulu2 \\\n --tasks wildguardtest,harmbench,toxigen:tiny \\\n --report_output_path ./generation_results/metrics.json \\\n --save_individual_results_path ./generation_results/all.json\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# run an OpenAI API model specific generation benchmarks by a single command. here, we use three benchmarks.\npython evaluation/eval.py generators \\\n --model_name_or_path openai:gpt-4 \\\n --model_input_template_path_or_name None \\\n --tasks wildguardtest,harmbench,toxigen:tiny \\\n --report_output_path ./generation_results/metrics.json \\\n --save_individual_results_path ./generation_results/all.json\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e run an OpenAI API model specific generation benchmarks by a single command. here, we use three benchmarks.\u003c/span\u003e\npython evaluation/eval.py generators \\\n --model_name_or_path openai:gpt-4 \\\n --model_input_template_path_or_name None \\\n --tasks wildguardtest,harmbench,toxigen:tiny \\\n --report_output_path ./generation_results/metrics.json \\\n --save_individual_results_path ./generation_results/all.json\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003cem\u003eSafety Classifier\u003c/em\u003e Evaluation\u003c/h2\u003e\u003ca id=\"user-content-safety-classifier-evaluation\" class=\"anchor\" aria-label=\"Permalink: Safety Classifier Evaluation\" href=\"#safety-classifier-evaluation\"\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\"\u003ePrompt harmfulness benchmarks\u003c/h3\u003e\u003ca id=\"user-content-prompt-harmfulness-benchmarks\" class=\"anchor\" aria-label=\"Permalink: Prompt harmfulness benchmarks\" href=\"#prompt-harmfulness-benchmarks\"\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/2406.18495\" rel=\"nofollow\"\u003eWildGuardTest\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2310.17389\" rel=\"nofollow\"\u003eToxicChat\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ojs.aaai.org/index.php/AAAI/article/view/26752\" rel=\"nofollow\"\u003eOpenAI Moderation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2404.05993\" rel=\"nofollow\"\u003eAegisSafetyTest\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2311.08370\" rel=\"nofollow\"\u003eSimpleSafetyTests\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2402.04249\" rel=\"nofollow\"\u003eHarmbench Prompt\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\"\u003eResponse harmfulness benchmarks\u003c/h3\u003e\u003ca id=\"user-content-response-harmfulness-benchmarks\" class=\"anchor\" aria-label=\"Permalink: Response harmfulness benchmarks\" href=\"#response-harmfulness-benchmarks\"\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/2406.18495\" rel=\"nofollow\"\u003eWildGuardTest\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2402.04249\" rel=\"nofollow\"\u003eHarmbench Response\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2406.15513\" rel=\"nofollow\"\u003eSafeRLHF\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2307.04657\" rel=\"nofollow\"\u003eBeaverTails\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2406.18495\" rel=\"nofollow\"\u003eXSTest-Resp\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\"\u003eResponse refusal benchmarks\u003c/h3\u003e\u003ca id=\"user-content-response-refusal-benchmarks\" class=\"anchor\" aria-label=\"Permalink: Response refusal benchmarks\" href=\"#response-refusal-benchmarks\"\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/2406.18495\" rel=\"nofollow\"\u003eWildGuardTest\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2406.18495\" rel=\"nofollow\"\u003eXSTest-Resp\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\"\u003eHow-to-use\u003c/h3\u003e\u003ca id=\"user-content-how-to-use-1\" class=\"anchor\" aria-label=\"Permalink: How-to-use\" href=\"#how-to-use-1\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe commands below allow for running benchmarks to evaluate quality of safety classifiers such as WildGuard and LlamaGuard. The first command can be used to run all included benchmarks, while the second can be used to run select benchmarks.\nSimilar to generation evals, to specify a task, the syntax is \u003ccode\u003e\u0026lt;folder\u0026gt;:\u0026lt;config_yaml\u0026gt;\u003c/code\u003e,\nwhere \u003ccode\u003efolder\u003c/code\u003e is a folder under \u003ccode\u003etasks/classificaiton\u003c/code\u003e and \u003ccode\u003econfig_yaml\u003c/code\u003e is the name of the configuration yaml file excluding \u003ccode\u003e.yaml\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"\n# run all classification benchmarks by a single command\n\nexport CUDA_VISIBLE_DEVICES={NUM_GPUS};\npython evaluation/run_all_classification_benchmarks.py \\\n --model_name WildGuard \\\n --report_output_path ./classification_results/metrics.json \\\n --save_individual_results_path ./classification_results/all.json\n\n# run specific classification benchmarks by a single command. here, we use four benchmarks\n\npython evaluation/eval.py classifiers \\\n --model_name WildGuard \\\n --tasks wildguardtest_prompt,wildguardtest_response,wildguardtest_refusal,openai_mod \\\n --report_output_path ./classification_results/metrics.json \\\n --save_individual_results_path ./classification_results/all.json\n\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e\n# run all classification benchmarks by a single command\n\nexport CUDA_VISIBLE_DEVICES={NUM_GPUS};\npython evaluation/run_all_classification_benchmarks.py \\\n --model_name WildGuard \\\n --report_output_path ./classification_results/metrics.json \\\n --save_individual_results_path ./classification_results/all.json\n\n# run specific classification benchmarks by a single command. here, we use four benchmarks\n\npython evaluation/eval.py classifiers \\\n --model_name WildGuard \\\n --tasks wildguardtest_prompt,wildguardtest_response,wildguardtest_refusal,openai_mod \\\n --report_output_path ./classification_results/metrics.json \\\n --save_individual_results_path ./classification_results/all.json\n\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAcknowledgements\u003c/h2\u003e\u003ca id=\"user-content-acknowledgements\" class=\"anchor\" aria-label=\"Permalink: Acknowledgements\" href=\"#acknowledgements\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis repository uses some code from the:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/centerforaisafety/HarmBench\"\u003eHarmbench\u003c/a\u003e -- in particular, code for model input templates,\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/allenai/open-instruct\"\u003eOpen-instruct\u003c/a\u003e -- in particular, code for model generation (general capabilities) benchmarks.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@misc{wildteaming2024,\n title={WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models}, \n author={Liwei Jiang and Kavel Rao and Seungju Han and Allyson Ettinger and Faeze Brahman and Sachin Kumar and Niloofar Mireshghallah and Ximing Lu and Maarten Sap and Yejin Choi and Nouha Dziri},\n year={2024},\n eprint={2406.18510},\n archivePrefix={arXiv},\n primaryClass={cs.CL},\n url={https://arxiv.org/abs/2406.18510}, \n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@misc{wildteaming2024,\n title={WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models}, \n author={Liwei Jiang and Kavel Rao and Seungju Han and Allyson Ettinger and Faeze Brahman and Sachin Kumar and Niloofar Mireshghallah and Ximing Lu and Maarten Sap and Yejin Choi and Nouha Dziri},\n year={2024},\n eprint={2406.18510},\n archivePrefix={arXiv},\n primaryClass={cs.CL},\n url={https://arxiv.org/abs/2406.18510}, \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=\"@misc{wildguard2024,\n title={WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs}, \n author={Seungju Han and Kavel Rao and Allyson Ettinger and Liwei Jiang and Bill Yuchen Lin and Nathan Lambert and Yejin Choi and Nouha Dziri},\n year={2024},\n eprint={2406.18495},\n archivePrefix={arXiv},\n primaryClass={cs.CL},\n url={https://arxiv.org/abs/2406.18495}, \n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@misc{wildguard2024,\n title={WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs}, \n author={Seungju Han and Kavel Rao and Allyson Ettinger and Liwei Jiang and Bill Yuchen Lin and Nathan Lambert and Yejin Choi and Nouha Dziri},\n year={2024},\n eprint={2406.18495},\n archivePrefix={arXiv},\n primaryClass={cs.CL},\n url={https://arxiv.org/abs/2406.18495}, \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":"Ai2 Safety Tool 🧰 (Evaluation Suite)","anchor":"ai2-safety-tool--evaluation-suite","htmlText":"Ai2 Safety Tool 🧰 (Evaluation Suite)"},{"level":2,"text":"Updates","anchor":"updates","htmlText":"Updates"},{"level":2,"text":"Features","anchor":"features","htmlText":"Features"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":2,"text":"Generative LM Evaluation","anchor":"generative-lm-evaluation","htmlText":"Generative LM Evaluation"},{"level":3,"text":"Safety benchmarks","anchor":"safety-benchmarks","htmlText":"Safety benchmarks"},{"level":3,"text":"General capabilities benchmarks","anchor":"general-capabilities-benchmarks","htmlText":"General capabilities benchmarks"},{"level":3,"text":"How-to-use","anchor":"how-to-use","htmlText":"How-to-use"},{"level":2,"text":"Safety Classifier Evaluation","anchor":"safety-classifier-evaluation","htmlText":"Safety Classifier Evaluation"},{"level":3,"text":"Prompt harmfulness benchmarks","anchor":"prompt-harmfulness-benchmarks","htmlText":"Prompt harmfulness benchmarks"},{"level":3,"text":"Response harmfulness benchmarks","anchor":"response-harmfulness-benchmarks","htmlText":"Response harmfulness benchmarks"},{"level":3,"text":"Response refusal benchmarks","anchor":"response-refusal-benchmarks","htmlText":"Response refusal benchmarks"},{"level":3,"text":"How-to-use","anchor":"how-to-use-1","htmlText":"How-to-use"},{"level":2,"text":"Acknowledgements","anchor":"acknowledgements","htmlText":"Acknowledgements"},{"level":2,"text":"Citation","anchor":"citation","htmlText":"Citation"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fallenai%2Fsafety-eval"}},{"displayName":"LICENSE.md","repoName":"safety-eval","refName":"main","path":"LICENSE.md","preferredFileType":"license","tabName":"License","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fallenai%2Fsafety-eval"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"blob_edit_unsaved_changes_storage":true,"accessible_code_button":true}}}}</script> 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data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Ai2 Safety Tool 🧰 (Evaluation Suite)</h1><a id="user-content-ai2-safety-tool--evaluation-suite" class="anchor" aria-label="Permalink: Ai2 Safety Tool 🧰 (Evaluation Suite)" href="#ai2-safety-tool--evaluation-suite"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This repository contains code for easy and comprehensive safety evaluation on generative LMs and safety moderation tools. This evaluation framework is used in safety projects at Ai2, including:</p> <ul dir="auto"> <li>WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models <ul dir="auto"> <li><a href="https://arxiv.org/abs/2406.18510" rel="nofollow"><img src="https://camo.githubusercontent.com/d2fef28a9e196cee4529bddd6443ede19de6e45c64743c2f702540af81a8c398/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09f939d2d70617065722d626c7565" data-canonical-src="https://img.shields.io/badge/📝-paper-blue" style="max-width: 100%;"></a> <a href="https://github.com/allenai/wildteaming"><img src="https://camo.githubusercontent.com/4da4d8b419c5146973309c3fd5e5660e50a3f6382be8fd286b3bb6976058d44a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09f94972d636f64652d726564" data-canonical-src="https://img.shields.io/badge/🔗-code-red" style="max-width: 100%;"></a> <a href="https://huggingface.co/allenai/llama2-7b-WildJailbreak" rel="nofollow"><img src="https://camo.githubusercontent.com/9ac3e3d2b807c98bfb4b7c9bd4a389581f1f86840ec18d9863bd41b85544f824/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09fa4972d74756c75322d2d37622d2d77696c646a61696c627265616b20286d6f64656c292d677265656e" data-canonical-src="https://img.shields.io/badge/🤗-tulu2--7b--wildjailbreak (model)-green" style="max-width: 100%;"></a> <a href="https://huggingface.co/allenai/llama2-13b-WildJailbreak" rel="nofollow"><img src="https://camo.githubusercontent.com/763a4849a8855a35fcba558d634d229e436a9ec16850b444322e4b8d3dfa4e81/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09fa4972d74756c75322d2d3133622d2d77696c646a61696c627265616b20286d6f64656c292d677265656e" data-canonical-src="https://img.shields.io/badge/🤗-tulu2--13b--wildjailbreak (model)-green" style="max-width: 100%;"></a> <a href="https://huggingface.co/datasets/allenai/wildjailbreak" rel="nofollow"> <img src="https://camo.githubusercontent.com/ea8d4e2cbf16a53ea8d9dc34f0c4ec2fa7a6f13f0d9dc436b4db34a023232aed/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09fa4972d77696c646a61696c627265616b202864617461292d6f72616e6765" data-canonical-src="https://img.shields.io/badge/🤗-wildjailbreak (data)-orange" style="max-width: 100%;"></a></li> </ul> </li> <li>WildGuard: Open One-stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs <ul dir="auto"> <li><a href="https://arxiv.org/abs/2406.18495" rel="nofollow"><img src="https://camo.githubusercontent.com/d2fef28a9e196cee4529bddd6443ede19de6e45c64743c2f702540af81a8c398/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09f939d2d70617065722d626c7565" data-canonical-src="https://img.shields.io/badge/📝-paper-blue" style="max-width: 100%;"></a> <a href="https://github.com/allenai/wildguard"><img src="https://camo.githubusercontent.com/4da4d8b419c5146973309c3fd5e5660e50a3f6382be8fd286b3bb6976058d44a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09f94972d636f64652d726564" data-canonical-src="https://img.shields.io/badge/🔗-code-red" style="max-width: 100%;"></a> <a href="https://huggingface.co/allenai/wildguard" rel="nofollow"><img src="https://camo.githubusercontent.com/6a3fa1c7b0e2148534b8c5f2086c177169387bfdf24da4380dc17fa980fd828c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09fa4972d77696c64677561726420286d6f64656c292d677265656e" data-canonical-src="https://img.shields.io/badge/🤗-wildguard (model)-green" style="max-width: 100%;"></a> <a href="https://huggingface.co/datasets/allenai/wildguardmix" rel="nofollow"><img src="https://camo.githubusercontent.com/76c1967129423e4635ab79998d22df225f8d69f2ad99e0a39246859adea6a606/68747470733a2f2f696d672e736869656c64732e696f2f62616467652ff09fa4972d77696c6467756172646d6978202864617461292d6f72616e6765" data-canonical-src="https://img.shields.io/badge/🤗-wildguardmix (data)-orange" style="max-width: 100%;"></a></li> </ul> </li> </ul> <p dir="auto">This lets you to display how the performance changes across more than 14 benchmarks as below 🔥</p> <a target="_blank" rel="noopener noreferrer" href="https://private-user-images.githubusercontent.com/162524131/350415777-7d29f65f-ab6a-4164-8adc-0c1dc860bb30.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MzIzNTgxMDksIm5iZiI6MTczMjM1NzgwOSwicGF0aCI6Ii8xNjI1MjQxMzEvMzUwNDE1Nzc3LTdkMjlmNjVmLWFiNmEtNDE2NC04YWRjLTBjMWRjODYwYmIzMC5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQxMTIzJTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MTEyM1QxMDMwMDlaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT0xZTdhYWRmN2U0ZjdkM2U5MTE5YjllYjBiNDZjM2E4ZjUzOGE1ZTM5ZDViOWY0NTg1MDBhYzVlZDU5NDFkNTdlJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCJ9.oVxHwCKkbT6Hu5HF2HjY9wIgRLk-gyhxeLW1yCZW-78"><img width="610" alt="image" src="https://private-user-images.githubusercontent.com/162524131/350415777-7d29f65f-ab6a-4164-8adc-0c1dc860bb30.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MzIzNTgxMDksIm5iZiI6MTczMjM1NzgwOSwicGF0aCI6Ii8xNjI1MjQxMzEvMzUwNDE1Nzc3LTdkMjlmNjVmLWFiNmEtNDE2NC04YWRjLTBjMWRjODYwYmIzMC5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQxMTIzJTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MTEyM1QxMDMwMDlaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT0xZTdhYWRmN2U0ZjdkM2U5MTE5YjllYjBiNDZjM2E4ZjUzOGE1ZTM5ZDViOWY0NTg1MDBhYzVlZDU5NDFkNTdlJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCJ9.oVxHwCKkbT6Hu5HF2HjY9wIgRLk-gyhxeLW1yCZW-78" style="max-width: 100%;"></a> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Updates</h2><a id="user-content-updates" class="anchor" aria-label="Permalink: Updates" href="#updates"><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>2024-07-06: add support for MMLU, TruthfulQA, and sorrybench classifier.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Features</h2><a id="user-content-features" class="anchor" aria-label="Permalink: Features" href="#features"><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>Easy evaluation of <strong>generative language models</strong> on list of <em>safety</em> benchmarks and <em>general</em> capabilities benchmarks such as MT-Bench and AlpacaEval2.</li> <li>Easy evaluation of <strong>safety classifiers</strong> on three tasks (detection of <em>prompt harmfulness</em>, <em>response harmfulness</em>, and <em>response refusal</em>) across 10+ benchmarks.</li> <li>Easy addition of new benchmarks and models to the evaluation suite.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><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="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="conda create -n safety-eval python=3.10 && conda activate safety-eval pip install -e . pip install -r requirements.txt pip install vllm==0.4.2"><pre>conda create -n safety-eval python=3.10 <span class="pl-k">&&</span> conda activate safety-eval pip install -e <span class="pl-c1">.</span> pip install -r requirements.txt pip install vllm==0.4.2</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto"><em>Generative</em> LM Evaluation</h2><a id="user-content-generative-lm-evaluation" class="anchor" aria-label="Permalink: Generative LM Evaluation" href="#generative-lm-evaluation"><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">Safety benchmarks</h3><a id="user-content-safety-benchmarks" class="anchor" aria-label="Permalink: Safety benchmarks" href="#safety-benchmarks"><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">For all benchmarks requiring safety classification unless noted otherwise, as a default, we use the <a href="https://github.com/allenai/wildguard">WildGuard</a> classifier to evaluate the safety of model outputs.</p> <ul dir="auto"> <li><a href="https://arxiv.org/abs/2406.18495" rel="nofollow">WildGuardTest</a></li> <li><a href="https://arxiv.org/abs/2402.04249" rel="nofollow">Harmbench</a></li> <li><a href="https://arxiv.org/abs/2203.09509" rel="nofollow">ToxiGen</a>: use <code>tomh/toxigen_roberta</code> as the classifier</li> <li><a href="https://arxiv.org/abs/2308.01263" rel="nofollow">XSTest</a></li> <li><a href="https://arxiv.org/abs/2401.05561" rel="nofollow">JailbreakTrigger (in TrustLLM)</a></li> <li><a href="https://arxiv.org/abs/2308.03825" rel="nofollow">Do-anything-now</a></li> <li><a href="https://arxiv.org/abs/2406.18510" rel="nofollow">WildJailbreak</a> (both harmful and benign contrast sets)</li> </ul> <p dir="auto"><strong>Changing classifiers for safety benchmarks</strong>:</p> <p dir="auto">You can change the safety classifier used for evaluation by specifying the <code>classifier_model_name</code> in the yaml file. For example, when you want to use the HarmBench's classifiers for evaluation on HarmBench, you can use <code>HarmbenchClassifier</code> as the <code>classifier_model_name</code>. Please check out the <code>evaluation/tasks/generation/harmbench/default.yaml</code> and <code>evaluation/tasks/classification/harmbench/harmbench_classsifier.yaml</code> to see the classifier's specification.</p> <div class="highlight highlight-source-yaml notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# evaluation/tasks/classification/harmbench/harmbench_classsifier.yaml task_class: HarmbenchVanilla classifier_model_name: HarmbenchClassifier # evaluation/tasks/generation/harmbench/default.yaml task_class: HarmbenchVanilla classifier_model_name: WildGuard"><pre><span class="pl-c"><span class="pl-c">#</span> evaluation/tasks/classification/harmbench/harmbench_classsifier.yaml</span> <span class="pl-ent">task_class</span>: <span class="pl-s">HarmbenchVanilla</span> <span class="pl-ent">classifier_model_name</span>: <span class="pl-s">HarmbenchClassifier</span> <span class="pl-c"><span class="pl-c">#</span> evaluation/tasks/generation/harmbench/default.yaml</span> <span class="pl-ent">task_class</span>: <span class="pl-s">HarmbenchVanilla</span> <span class="pl-ent">classifier_model_name</span>: <span class="pl-s">WildGuard</span></pre></div> <p dir="auto">Please refer to <code>src/classifier_models/</code> directory to explore the classifiers implementation.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">General capabilities benchmarks</h3><a id="user-content-general-capabilities-benchmarks" class="anchor" aria-label="Permalink: General capabilities benchmarks" href="#general-capabilities-benchmarks"><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">Optimal safety training maintains or even improves models' general capabilities. We include general capability evaluation for monitoring this dimension of safety training.</p> <ul dir="auto"> <li><a href="https://arxiv.org/abs/2404.04475" rel="nofollow">AlpacaEval (V2)</a></li> <li><a href="https://arxiv.org/abs/2306.05685" rel="nofollow">MTBench</a></li> <li><a href="https://arxiv.org/abs/2110.14168" rel="nofollow">GSM8K</a></li> <li><a href="https://arxiv.org/abs/2210.09261" rel="nofollow">Big Bench Hard (BBH)</a></li> <li><a href="https://arxiv.org/abs/2107.03374" rel="nofollow">Codex-Eval</a></li> <li><a href="https://arxiv.org/abs/2009.03300" rel="nofollow">MMLU</a></li> <li><a href="https://arxiv.org/abs/2109.07958" rel="nofollow">TruthfulQA</a></li> </ul> <p dir="auto">Support for additional benchmarks, including <a href="https://arxiv.org/abs/2311.07911" rel="nofollow">IFEval</a>, and <a href="https://arxiv.org/abs/2003.05002" rel="nofollow">TyDiQA</a> is in progress. For TydiQA, please use <a href="https://github.com/allenai/open-instruct">open-instruct</a> to evaluate the models for now.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">How-to-use</h3><a id="user-content-how-to-use" class="anchor" aria-label="Permalink: How-to-use" href="#how-to-use"><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">Below are commands to run safety and general capability benchmarking for generative LMs. The first command can be used to run all included benchmarks for models which support vLLM. The second command can be used to select individual benchmarks for evaluation. To specify a task, the syntax is <code><folder>:<config_yaml></code>, where <code>folder</code> is a folder under <code>tasks/generation</code> and <code>config_yaml</code> is the name of the configuration yaml file excluding <code>.yaml</code>.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# run all generation benchmarks by a single command. assume you are using vllm. # note that you should add OPENAI_API_KEY to your environment variables when you use mtbench and alpacaeval2. export CUDA_VISIBLE_DEVICES={NUM_GPUS}; python evaluation/run_all_generation_benchmarks.py \ --model_name_or_path allenai/tulu-2-dpo-7b \ --model_input_template_path_or_name tulu2 \ --report_output_path ./generation_results/metrics.json \ --save_individual_results_path ./generation_results/all.json # run specific generation benchmarks by a single command. here, we use three benchmarks. python evaluation/eval.py generators \ --use_vllm \ --model_name_or_path allenai/tulu-2-dpo-7b \ --model_input_template_path_or_name tulu2 \ --tasks wildguardtest,harmbench,toxigen:tiny \ --report_output_path ./generation_results/metrics.json \ --save_individual_results_path ./generation_results/all.json"><pre><span class="pl-c"><span class="pl-c">#</span> run all generation benchmarks by a single command. assume you are using vllm. </span> <span class="pl-c"><span class="pl-c">#</span> note that you should add OPENAI_API_KEY to your environment variables when you use mtbench and alpacaeval2.</span> <span class="pl-k">export</span> CUDA_VISIBLE_DEVICES={NUM_GPUS}<span class="pl-k">;</span> python evaluation/run_all_generation_benchmarks.py \ --model_name_or_path allenai/tulu-2-dpo-7b \ --model_input_template_path_or_name tulu2 \ --report_output_path ./generation_results/metrics.json \ --save_individual_results_path ./generation_results/all.json <span class="pl-c"><span class="pl-c">#</span> run specific generation benchmarks by a single command. here, we use three benchmarks.</span> python evaluation/eval.py generators \ --use_vllm \ --model_name_or_path allenai/tulu-2-dpo-7b \ --model_input_template_path_or_name tulu2 \ --tasks wildguardtest,harmbench,toxigen:tiny \ --report_output_path ./generation_results/metrics.json \ --save_individual_results_path ./generation_results/all.json</pre></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# run an OpenAI API model specific generation benchmarks by a single command. here, we use three benchmarks. python evaluation/eval.py generators \ --model_name_or_path openai:gpt-4 \ --model_input_template_path_or_name None \ --tasks wildguardtest,harmbench,toxigen:tiny \ --report_output_path ./generation_results/metrics.json \ --save_individual_results_path ./generation_results/all.json"><pre><span class="pl-c"><span class="pl-c">#</span> run an OpenAI API model specific generation benchmarks by a single command. here, we use three benchmarks.</span> python evaluation/eval.py generators \ --model_name_or_path openai:gpt-4 \ --model_input_template_path_or_name None \ --tasks wildguardtest,harmbench,toxigen:tiny \ --report_output_path ./generation_results/metrics.json \ --save_individual_results_path ./generation_results/all.json</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto"><em>Safety Classifier</em> Evaluation</h2><a id="user-content-safety-classifier-evaluation" class="anchor" aria-label="Permalink: Safety Classifier Evaluation" href="#safety-classifier-evaluation"><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">Prompt harmfulness benchmarks</h3><a id="user-content-prompt-harmfulness-benchmarks" class="anchor" aria-label="Permalink: Prompt harmfulness benchmarks" href="#prompt-harmfulness-benchmarks"><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/2406.18495" rel="nofollow">WildGuardTest</a></li> <li><a href="https://arxiv.org/abs/2310.17389" rel="nofollow">ToxicChat</a></li> <li><a href="https://ojs.aaai.org/index.php/AAAI/article/view/26752" rel="nofollow">OpenAI Moderation</a></li> <li><a href="https://arxiv.org/abs/2404.05993" rel="nofollow">AegisSafetyTest</a></li> <li><a href="https://arxiv.org/abs/2311.08370" rel="nofollow">SimpleSafetyTests</a></li> <li><a href="https://arxiv.org/abs/2402.04249" rel="nofollow">Harmbench Prompt</a></li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Response harmfulness benchmarks</h3><a id="user-content-response-harmfulness-benchmarks" class="anchor" aria-label="Permalink: Response harmfulness benchmarks" href="#response-harmfulness-benchmarks"><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/2406.18495" rel="nofollow">WildGuardTest</a></li> <li><a href="https://arxiv.org/abs/2402.04249" rel="nofollow">Harmbench Response</a></li> <li><a href="https://arxiv.org/abs/2406.15513" rel="nofollow">SafeRLHF</a></li> <li><a href="https://arxiv.org/abs/2307.04657" rel="nofollow">BeaverTails</a></li> <li><a href="https://arxiv.org/abs/2406.18495" rel="nofollow">XSTest-Resp</a></li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Response refusal benchmarks</h3><a id="user-content-response-refusal-benchmarks" class="anchor" aria-label="Permalink: Response refusal benchmarks" href="#response-refusal-benchmarks"><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/2406.18495" rel="nofollow">WildGuardTest</a></li> <li><a href="https://arxiv.org/abs/2406.18495" rel="nofollow">XSTest-Resp</a></li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">How-to-use</h3><a id="user-content-how-to-use-1" class="anchor" aria-label="Permalink: How-to-use" href="#how-to-use-1"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The commands below allow for running benchmarks to evaluate quality of safety classifiers such as WildGuard and LlamaGuard. The first command can be used to run all included benchmarks, while the second can be used to run select benchmarks. Similar to generation evals, to specify a task, the syntax is <code><folder>:<config_yaml></code>, where <code>folder</code> is a folder under <code>tasks/classificaiton</code> and <code>config_yaml</code> is the name of the configuration yaml file excluding <code>.yaml</code>.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content=" # run all classification benchmarks by a single command export CUDA_VISIBLE_DEVICES={NUM_GPUS}; python evaluation/run_all_classification_benchmarks.py \ --model_name WildGuard \ --report_output_path ./classification_results/metrics.json \ --save_individual_results_path ./classification_results/all.json # run specific classification benchmarks by a single command. here, we use four benchmarks python evaluation/eval.py classifiers \ --model_name WildGuard \ --tasks wildguardtest_prompt,wildguardtest_response,wildguardtest_refusal,openai_mod \ --report_output_path ./classification_results/metrics.json \ --save_individual_results_path ./classification_results/all.json "><pre class="notranslate"><code> # run all classification benchmarks by a single command export CUDA_VISIBLE_DEVICES={NUM_GPUS}; python evaluation/run_all_classification_benchmarks.py \ --model_name WildGuard \ --report_output_path ./classification_results/metrics.json \ --save_individual_results_path ./classification_results/all.json # run specific classification benchmarks by a single command. here, we use four benchmarks python evaluation/eval.py classifiers \ --model_name WildGuard \ --tasks wildguardtest_prompt,wildguardtest_response,wildguardtest_refusal,openai_mod \ --report_output_path ./classification_results/metrics.json \ --save_individual_results_path ./classification_results/all.json </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Acknowledgements</h2><a id="user-content-acknowledgements" class="anchor" aria-label="Permalink: Acknowledgements" href="#acknowledgements"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This repository uses some code from the:</p> <ul dir="auto"> <li><a href="https://github.com/centerforaisafety/HarmBench">Harmbench</a> -- in particular, code for model input templates,</li> <li><a href="https://github.com/allenai/open-instruct">Open-instruct</a> -- in particular, code for model generation (general capabilities) benchmarks.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citation</h2><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@misc{wildteaming2024, title={WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models}, author={Liwei Jiang and Kavel Rao and Seungju Han and Allyson Ettinger and Faeze Brahman and Sachin Kumar and Niloofar Mireshghallah and Ximing Lu and Maarten Sap and Yejin Choi and Nouha Dziri}, year={2024}, eprint={2406.18510}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2406.18510}, }"><pre class="notranslate"><code>@misc{wildteaming2024, title={WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models}, author={Liwei Jiang and Kavel Rao and Seungju Han and Allyson Ettinger and Faeze Brahman and Sachin Kumar and Niloofar Mireshghallah and Ximing Lu and Maarten Sap and Yejin Choi and Nouha Dziri}, year={2024}, eprint={2406.18510}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2406.18510}, } </code></pre></div> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@misc{wildguard2024, title={WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs}, author={Seungju Han and Kavel Rao and Allyson Ettinger and Liwei Jiang and Bill Yuchen Lin and Nathan Lambert and Yejin Choi and Nouha Dziri}, year={2024}, eprint={2406.18495}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2406.18495}, }"><pre class="notranslate"><code>@misc{wildguard2024, title={WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs}, author={Seungju Han and Kavel Rao and Allyson Ettinger and Liwei Jiang and Bill Yuchen Lin and Nathan Lambert and Yejin Choi and Nouha Dziri}, year={2024}, eprint={2406.18495}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2406.18495}, } </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="qxzqcgTFvJmX372Kv0jYNn2QmZ6EedZnnvrCkQJGNawD6WDnFQ96pEpYT+l1uTksrK9oNzFLTxnavQwp5KXYBg==" /> </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"> A simple evaluation of generative language models and safety classifiers. </p> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a class="Link--muted" data-analytics-event="{"category":"Repository Overview","action":"click","label":"location:sidebar;file:readme"}" href="#readme-ov-file"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" 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dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>