38 citations · 48 across the 27 of their papers we have counts for
5 papers · 1 filter
EnGRICH: Enhancing Generative Reward Modeling with Critiques from Humans
Xuancheng Li, Beining Wang, Haitao Li +7
Generative reward models (GRMs) are important for LLM optimization. Unlike scalar reward models, GRMs generate natural-language critiques alongside preference judgments, providing…
Different Feedback, Different Updates: Selective Self-Learning from User Interactions for Large Language Models
Xuancheng Li, Xuanchen Li, Haitao Li +6
User feedback offers natural supervision for persistent LLM improvement, but a single message may support multiple behavioral changes with different scopes of generalization. We in…
How do Humans Process AI-generated Hallucination Contents: a Neuroimaging Study
Shuqi Zhu, Yi Zhong, Ziyi Ye +4
While AI-generated hallucinations pose considerable risks, the underlying cognitive mechanisms by which humans can successfully recognize or be misled by these hallucinations remai…
MulFeRL: Enhancing Reinforcement Learning with Verbal Feedback in a Multi-turn Loop
Xuancheng Li, Haitao Li, Yujia Zhou +2
Reinforcement Learning with Verifiable Rewards (RLVR) is widely used to improve reasoning across domains, but outcome-only scalar rewards are often sparse and uninformative. This l…
From <Answer> to <Think>: Multidimensional Supervision of Reasoning Process for LLM Optimization
Beining Wang, Weihang Su, Hongtao Tian +5
Improving the multi-step reasoning ability of Large Language Models (LLMs) is a critical yet challenging task. The dominant paradigm, outcome-supervised reinforcement learning (RLV…