@inproceedings{zhang-etal-2025-dynamic-evaluation,
    title = "Dynamic Evaluation with Cognitive Reasoning for Multi-turn Safety of Large Language Models",
    author = "Zhang, Lanxue  and
      Cao, Yanan  and
      Xie, Yuqiang  and
      Fang, Fang  and
      Li, Yangxi",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.963/",
    pages = "19588--19608",
    ISBN = "979-8-89176-251-0",
    abstract = "The rapid advancement of Large Language Models (LLMs) poses significant challenges for safety evaluation. Current static datasets struggle to identify emerging vulnerabilities due to three limitations: (1) they risk being exposed in model training data, leading to evaluation bias; (2) their limited prompt diversity fails to capture real-world application scenarios; (3) they are limited to provide human-like multi-turn interactions. To address these limitations, we propose a dynamic evaluation framework, CogSafe, for comprehensive and automated multi-turn safety assessment of LLMs. We introduce CogSafe based on cognitive theories to simulate the real chatting process. To enhance assessment diversity, we introduce scenario simulation and strategy decision to guide the dynamic generation, enabling coverage of application situations. Furthermore, we incorporate the cognitive process to simulate multi-turn dialogues that reflect the cognitive dynamics of real-world interactions. Extensive experiments demonstrate the scalability and effectiveness of our framework, which has been applied to evaluate the safety of widely used LLMs."
}