WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models
<|reference_start|>WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models: 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.<|reference_end|>
@article{gupta2024walledeval:,
    title={WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language
  Models},
    author={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},
    journal={arXiv preprint arXiv:2408.03837},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03837},
    primaryClass={cs.CL cs.AI}
}