WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models

Prannaya Gupta, Yau Le Qi, Low Hao Han, Lee I-Shiang, Hugo Maximus Lim, Yu Xin Teoh, Jia Hng Koh, Dar Win Liew, Rishabh Bhardwaj, Rajat Bhardwaj, Soujanya Poria

WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models: 18 upvotes on Hugging Face Daily Papers, #4 of 11 papers on 2024-08-08. Day-by-day upvote history.

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 SGXSTest, a benchmark for assessing exaggerated safety in cultural contexts. We make WalledEval publicly available at https://github.com/walledai/walledevalA.

Paper page on Hugging Face · arXiv

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