Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
Yuhang Wu · Xiangqing Shen · Fanfan Wang · Cangqi Zhou · Zhen Wu · Xinyu Dai · Rui Xia
摘要
Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation. However, current reranking models are typically optimized on static human annotated relevance labels in isolation, decoupled from the downstream generation process. This isolation leads to a fundamenta...