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arXiv · 2511.06396

Efficient LLM Safety Evaluation through Multi-Agent Debate

Dachuan Lin·Guobin Shen·Zihao Yang·Tianrong Liu·Dongcheng Zhao·Yi Zeng
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Abstract

Safety evaluation of large language models (LLMs) increasingly relies on LLM-as-a-judge pipelines, but strong judges can still be expensive to use at scale. We study whether structured multi-agent debate can improve judge reliability while keeping backbone size and cost modest. To do so, we introduce HAJailBench, a human-annotated jailbreak benchmark with 11,100 labeled interactions spanning diverse attack methods and target models, and we pair it with a Multi-Agent Judge framework in which critic, defender, and judge agents debate under a shared safety rubric. On HAJailBench, the framework improves over matched small-model prompt baselines and prior multi-agent judges, while remaining more economical than GPT-4o under the evaluated pricing snapshot. Ablation results further show that a small number of debate rounds is sufficient to capture most of the gain. Together, these results support structured, value-aligned debate as a practical design for scalable LLM safety evaluation.

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Dachuan Lin, Guobin Shen, Zihao Yang, Tianrong Liu, Dongcheng Zhao, Yi Zeng. 2025-11-09. Efficient LLM Safety Evaluation through Multi-Agent Debate. https://arxiv.org/abs/2511.06396

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