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MedGUIDE: Benchmarking Clinical Decision-Making in Large Language Models
Xiaomin Li, Mingye Gao, Yuexing Hao +4
Clinical guidelines, typically structured as decision trees, are central to evidence-based medical practice and critical for ensuring safe and accurate diagnostic decision-making.…
ENCORE: Entropy-guided Reward Composition for Multi-head Safety Reward Models
Xiaomin Li, Xupeng Chen, Jingxuan Fan +2
The safety alignment of large language models (LLMs) often relies on reinforcement learning from human feedback (RLHF), which requires human annotations to construct preference dat…
Data-adaptive Safety Rules for Training Reward Models
Xiaomin Li, Mingye Gao, Zhiwei Zhang +2
Reinforcement Learning from Human Feedback (RLHF) is commonly employed to tailor models to human preferences, especially to improve the safety of outputs from large language models…
Catastrophic Failure of LLM Unlearning via Quantization
Zhiwei Zhang, Fali Wang, Xiaomin Li +6
Large language models (LLMs) have shown remarkable proficiency in generating text, benefiting from extensive training on vast textual corpora. However, LLMs may also acquire unwant…
Selection of LLM Fine-Tuning Data based on Orthogonal Rules
Xiaomin Li, Mingye Gao, Zhiwei Zhang +2
High-quality training data is critical to the performance of large language models (LLMs). Recent work has explored using LLMs to rate and select data based on a small set of human…