SafeLawBench: Towards Safe Alignment of Large Language Models
Chuxue Cao, Zhu Han, Jiaming Ji, Qichao Sun, Zhenghao Zhu, Wu Yinyu, Josef Dai, Yaodong Yang, Sirui Han, Yike Guo · 2025
With the growing prevalence of large language models (LLMs), the safety of LLMs has raised significant concerns.However, there is still a lack of definitive standards for evaluating their safety due to the subjective nature of current safety benchmarks.To address this gap, we conducted the first exploration of LLMs' safety evaluation from a legal perspective by proposing the SafeLawBench benchmark.SafeLawBench categorizes safety risks into three levels based on legal standards, providing a systematic and comprehensive framework for evaluation.It comprises 24,860 multi-choice questions and 1,106 open-domain question-answering (QA) tasks.Our evaluation included 2 closed-source LLMs and 18 open-source LLMs using zero-shot and fewshot prompting, highlighting the safety features of each model.We also evaluated the LLMs' safety-related reasoning stability and refusal behavior.Additionally, we found that a majority voting mechanism can enhance model performance.Notably, even leading SOTA models like Claude-3.5-Sonnetand GPT-4o have not exceeded 80.5% accuracy in multi-choice tasks on SafeLawBench, while the average accuracy of 20 LLMs remains at 68.8%.We urge the community to prioritize research on the safety of LLMs.Our dataset and code are available.1