2 citations · 3 across the 21 of their papers we have counts for
5 papers · 1 filter
Multiplayer Nash Preference Optimization
Fang Wu, Xu Huang, Weihao Xuan +8
Reinforcement learning from human feedback (RLHF) has emerged as the standard paradigm for aligning large language models with human preferences. However, reward-based methods grou…
Bradley-Terry and Multi-Objective Reward Modeling Are Complementary
Zhiwei Zhang, Hui Liu, Xiaomin Li +10
Reward models trained on human preference data have demonstrated strong effectiveness in aligning Large Language Models (LLMs) with human intent under the framework of Reinforcemen…
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…