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cs.CL2026
CAR: Query-Guided Confidence-Aware Reranking for Retrieval-Augmented Generation
Zhipeng Song, Yizhi Zhou, Xiangyu Kong +7
Retrieval-augmented generation (RAG) relies on evidence ranking to determine what information is exposed to the generator, yet existing retrieval and reranking methods primarily es…
cs.CL2026★ 1 cited
LLM-Confidence Reranker: A Training-Free Approach for Enhancing Retrieval-Augmented Generation Systems
Zhipeng Song, Xiangyu Kong, Xinrui Bao +5
Large language models (LLMs) have revolutionized natural language processing, yet hallucinations in knowledge-intensive tasks remain a critical challenge. Retrieval-augmented gener…
cs.CL2026
Less is More for RAG: Information Gain Pruning for Generator-Aligned Reranking and Evidence Selection
Zhipeng Song, Yizhi Zhou, Xiangyu Kong +6
Retrieval-augmented generation (RAG) grounds large language models with external evidence, but under a limited context budget, the key challenge is deciding which retrieved passage…