1 citations · 1 across the 10 of their papers we have counts for
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SPEAR: Distilling Domain-Adaptive Reasoning Skeletons via Sequential Symbolic Alignment in Reinforcement Learning
Zhuochun Li, Yuelyu Ji, Yiming Zeng +1
Reinforcement learning-based knowledge distillation has the potential to transfer complex reasoning from teacher to student models, yet it currently faces a critical dilemma: resea…
Retrieval--Reasoning Processes for Multi-hop Question Answering: A Four-Axis Design Framework and Empirical Trends
Yuelyu Ji, Zhuochun Li, Rui Meng +1
Multi-hop question answering (QA) requires systems to iteratively retrieve evidence and reason across multiple hops. While recent RAG and agentic methods report strong results, the…
Rethinking LLM-as-a-Judge: Representation-as-a-Judge with Small Language Models via Semantic Capacity Asymmetry
Zhuochun Li, Yong Zhang, Ming Li +8
Large language models (LLMs) are widely used as reference-free evaluators via prompting, but this "LLM-as-a-Judge" paradigm is costly, opaque, and sensitive to prompt design. In th…
Curriculum Guided Reinforcement Learning for Efficient Multi Hop Retrieval Augmented Generation
Yuelyu Ji, Rui Meng, Zhuochun Li +1
Retrieval-augmented generation (RAG) grounds large language models (LLMs) in up-to-date external evidence, yet existing multi-hop RAG pipelines still issue redundant subqueries, ex…
Memory-Aware and Uncertainty-Guided Retrieval for Multi-Hop Question Answering
Yuelyu Ji, Rui Meng, Zhuochun Li +1
Multi-hop question answering (QA) requires models to retrieve and reason over multiple pieces of evidence. While Retrieval-Augmented Generation (RAG) has made progress in this area…
Mitigating the Risk of Health Inequity Exacerbated by Large Language Models
Yuelyu Ji, Wenhe Ma, Sonish Sivarajkumar +6
Recent advancements in large language models have demonstrated their potential in numerous medical applications, particularly in automating clinical trial matching for translationa…