一作ACL 2026Poster· Natural Language Processing
Don’t Be Misled by Style: A Style-Adaptive Reranker for Capturing Effective Knowledge in Retrieval-Augmented Generation
Ruwen Zhang · Bo Liu · Zhang Sheng Xiang · Yida Chen · Hantao Zhao · Ding Ding · Jiahui Jin · Jiuxin Cao
摘要
Rerankers are critical in Retrieval-Augmented Generation (RAG) for filtering evidence that enhances the accurate generation of LLMs. With the extension to open-domain scenarios, rerankers are inevitably deployed on mixed-style corpora, whereas most existing rerankers are mainly trained on well-ed...