80 citations · 121 across the 25 of their papers we have counts for
27 papers
MOCC-R1: Reinforcing Reasoning-Response Consistency for Multimodal Counselor Response Generation
Wenjie Zheng, Qiming Xie, Jianfei Yu +1
Multimodal counselor response generation (MCRG) aims to generate an appropriate counselor response from multimodal dialogue histories. Progress is limited by two gaps: first, exist…
FARCA: Fact-Aligned Reliability-Aware Credit Assignment for Reinforcement Learning with Factual Supervision
Qiming Xie, Wenjie Zheng, Xiangqing Shen +1
To reduce the hallucination risk caused by outcome-driven rewards in large language models trained through reinforcement learning with verifiable rewards, existing mitigation appro…
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
Yuhang Wu, Xiangqing Shen, Fanfan Wang +4
Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation. However, current reranking models are typically optimized on static human annotated…
Execution-Verified Reinforcement Learning for Optimization Modeling
Runda Guan, Xiangqing Shen, Jiajun Zhang +3
Automating optimization modeling with LLMs is a promising path toward scalable decision intelligence, but existing approaches either rely on agentic pipelines built on closed-sourc…
ChainEdit: Propagating Ripple Effects in LLM Knowledge Editing through Logical Rule-Guided Chains
Zilu Dong, Xiangqing Shen, Zinong Yang +1
Current knowledge editing methods for large language models (LLMs) struggle to maintain logical consistency when propagating ripple effects to associated facts. We propose ChainEdi…
Reason-Align-Respond: Aligning LLM Reasoning with Knowledge Graphs for KGQA
Xiangqing Shen, Fanfan Wang, Rui Xia
LLMs have demonstrated remarkable capabilities in complex reasoning tasks, yet they often suffer from hallucinations and lack reliable factual grounding. Meanwhile, knowledge graph…