large language model evaluation 1preference learning 1query-only supervision 1rubric generation 1synthetic pairwise data 1
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cs.CL2026
Rubrics on Trial: Evolving Rubrics from a Single Query via Synthetic Pairwise Evidence
Haocheng Yang, Licheng Pan, Xiaoxi Li +5
The paper proposes a query‑only method that automatically creates and validates fine‑grained rubrics for evaluating large language models by using synthetic rubric‑conditioned resp…
cs.CL2026
Evaluating Chinese Ambiguity Understanding in Large Language Models
Junwen Mo, Yuanzhi Lu, Yifang Xue +2
Linguistic ambiguity is critical to the robustness of Large Language Models (LLMs), yet existing research focuses mostly on English, with limited attention devoted to Chinese. Exis…
cs.CL2026
ImplicitRM: Unbiased Reward Modeling from Implicit Preference Data for LLM alignment
Hao Wang, Haocheng Yang, Licheng Pan +7
Reward modeling represents a long-standing challenge in reinforcement learning from human feedback (RLHF) for aligning language models. Current reward modeling is heavily contingen…