3 citations · 3 across the 4 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
cs.AI2026
Rubric Judgments Lack Uncertainty: Conformal Weighting for LLM Evaluation
Licheng Pan, Haocheng Yang, Haoxuan Li +7
Rubric-based evaluation is widely used to assess large language model (LLM) outputs by decomposing quality into multiple criteria and assigning each a binary judgment from an LLM v…
cs.AI2026
CleanRM: Reward Modeling from Noisy Feedback with a Few Clean Feedback for RLHF
Licheng Pan, Haocheng Yang, Yuzhou Chen +7
Feedback noise is pervasive in preference data, arising from subjective human judgments, annotation mistakes, and imperfect LLM judges. Standard reward modeling trained on such fee…
cs.LG2026
Uncertainty-Aware Reward Modeling for Stable RLHF
Licheng Pan, Haocheng Yang, Haoxuan Li +7
Reinforcement learning from human feedback (RLHF) aligns large language models by training reward models on preference data and optimizing policies to maximize predicted rewards. H…