6 papers
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…
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…
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…
Learning from Committee: Reasoning Distillation from a Mixture of Teachers with Peer-Review
Zhuochun Li, Yuelyu Ji, Rui Meng +1
While reasoning capabilities typically emerge in large language models (LLMs) with tens of billions of parameters, recent research focuses on improving smaller open-source models t…
ReasoningRank: Teaching Student Models to Rank through Reasoning-Based Knowledge Distillation
Yuelyu Ji, Zhuochun Li, Rui Meng +1
Reranking documents based on their relevance to a given query is a critical task in information retrieval. Traditional reranking methods often lack transparency and rely on proprie…
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…