Reasoning-Induced Misalignment, where fine-tuning on reasoning data containing no harmful content, including mathematics, code, and problem-solving with chain-of-thought traces can induce harmful behaviors of LLM, posing a serious challenge to the safety of LLM reasoning. Cross-architecture, cross-scale, and cross-data...
Yipeng Zhao, Qi-Shun Yang, Shenzhe Zhu et al.· 0 citations
A user-centric framework for systematically auditing system prompts in AI systems, AISPA is introduced, a user-centric framework for systematically auditing system prompts in AI systems that examines specific parts of a system prompt and evaluates them along eight dimensions that matter to users.
Xiangning Lin, Shenzhe Zhu, Shu Yang et al.· arXiv.org· 0 citations
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