{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/walledeval-a-comprehensive-safety-evaluation","title":"WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models","arxiv_id":"2408.03837","date":"2024-08-07","proceeding":null,"authors":["Prannaya Gupta","Le Qi Yau","Hao Han Low","I-Shiang Lee","Hugo Maximus Lim","Yu Xin Teoh","Jia Hng Koh","Dar Win Liew","Rishabh Bhardwaj","Rajat Bhardwaj","Soujanya Poria"],"abstract":"WalledEval is a comprehensive AI safety testing toolkit designed to evaluate large language models (LLMs). It accommodates a diverse range of models, including both open-weight and API-based ones, and features over 35 safety benchmarks covering areas such as multilingual safety, exaggerated safety, and prompt injections. The framework supports both LLM and judge benchmarking and incorporates custom mutators to test safety against various text-style mutations, such as future tense and paraphrasing. Additionally, WalledEval introduces WalledGuard, a new, small, and performant content moderation tool, and two datasets: SGXSTest and HIXSTest, which serve as benchmarks for assessing the exaggerated safety of LLMs and judges in cultural contexts. We make WalledEval publicly available at https://github.com/walledai/walledeval.","url_abs":"https://arxiv.org/abs/2408.03837v3","url_pdf":"https://arxiv.org/pdf/2408.03837v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"walledeval-a-comprehensive-safety-evaluation","repo_url":"https://github.com/walledai/walledeval","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"ai-and-safety","task_name":"AI and Safety"},{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"instruction-following","task_name":"Instruction Following"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"large-language-model","task_name":"Large Language Model"}],"methods":[],"datasets_introduced":[{"slug":"hixstest","name":"HiXSTest","full_name":"Hindi XSTest"},{"slug":"sgxstest","name":"SGXSTest","full_name":"Singapore XSTest"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2408.03837","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}