Xinyu Mu — accepted AI-conference papers

2025

KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree Search

Haoran Luo, Haihong E, Yikai Guo, Qika Lin, Xiaobao Wu, Xinyu Mu +4

ICML 2025poster

Knowledge Base Question Answering (KBQA) aims to answer natural language questions with a large-scale structured knowledge base (KB). Despite advancements with large language models (LLMs), KBQA still faces challenges in weak KB awareness, imbalance between effectiveness and efficiency, and high rel…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗

2025

Training Language Model to Critique for Better Refinement

Tianshu Yu, Chao Xiang, Mingchuan Yang, Pei Ke, Bosi Wen, Cunxiang Wang +5

ACL 2025finding

Large language models (LLMs) have demonstrated remarkable evaluation and critique capabilities, providing insightful feedback and identifying flaws in various tasks. However, limited research has explored which types of critiques are most effective for improving model responses or how to generate su…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗