S-Eval: Towards Automated and Comprehensive Safety Evaluation for Large Language Models

• URL: http://arxiv.org/abs/2405.14191v4
• Date: Mon, 07 Apr 2025 07:52:28 GMT
• Title: S-Eval: Towards Automated and Comprehensive Safety Evaluation for Large Language Models
• Authors: Xiaohan Yuan, Jinfeng Li, Dongxia Wang, Yuefeng Chen, Xiaofeng Mao, Longtao Huang, Jialuo Chen, Hui Xue, Xiaoxia Liu, Wenhai Wang, Kui Ren, Jingyi Wang,
• Abstract summary: Generative large language models (LLMs) have revolutionized natural language processing with their transformative and emergent capabilities.<n>Recent evidence indicates that LLMs can produce harmful content that violates social norms.<n>We propose S-Eval, an automated Safety Evaluation framework with a newly defined comprehensive risk taxonomy.
• Score: 46.148439517272024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative large language models (LLMs) have revolutionized natural language processing with their transformative and emergent capabilities. However, recent evidence indicates that LLMs can produce harmful content that violates social norms, raising significant concerns regarding the safety and ethical ramifications of deploying these advanced models. Thus, it is both critical and imperative to perform a rigorous and comprehensive safety evaluation of LLMs before deployment. Despite this need, owing to the extensiveness of LLM generation space, it still lacks a unified and standardized risk taxonomy to systematically reflect the LLM content safety, as well as automated safety assessment techniques to explore the potential risk efficiently. To bridge the striking gap, we propose S-Eval, a novel LLM-based automated Safety Evaluation framework with a newly defined comprehensive risk taxonomy. S-Eval incorporates two key components, i.e., an expert testing LLM ${M}_t$ and a novel safety critique LLM ${M}_c$. ${M}_t$ is responsible for automatically generating test cases in accordance with the proposed risk taxonomy. ${M}_c$ can provide quantitative and explainable safety evaluations for better risk awareness of LLMs. In contrast to prior works, S-Eval is efficient and effective in test generation and safety evaluation. Moreover, S-Eval can be flexibly configured and adapted to the rapid evolution of LLMs and accompanying new safety threats, test generation methods and safety critique methods thanks to the LLM-based architecture. S-Eval has been deployed in our industrial partner for the automated safety evaluation of multiple LLMs serving millions of users, demonstrating its effectiveness in real-world scenarios. Our benchmark is publicly available at https://github.com/IS2Lab/S-Eval.

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