Dynamic Evaluation with Cognitive Reasoning for Multi-turn Safety of Large Language Models

ACL ARR 2025 February Submission6997 Authors

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.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: benchmarking; evaluation methodologies; evaluation; safety evaluation

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data resources, Data analysis

Languages Studied: English

Submission Number: 6997