R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models

arXiv:2405.02659v1 Announce Type: new
Abstract: Retrieval-augmented large language models (LLMs) leverage relevant content retrieved by information retrieval systems to generate correct responses, aiming to alleviate the hallucination problem. However, existing retriever-responder methods typically append relevant documents to the prompt of LLMs to perform text generation tasks without considering the interaction of fine-grained structural semantics between the retrieved documents and the LLMs. This issue is particularly important for accurate response generation as LLMs tend to ``lose in the middle'' when dealing …

abstract arxiv cs.cl documents fine-grained generate hallucination hallucination problem however information language language models large language large language models llms prompt responses retrieval retrieval-augmented retriever systems tasks text text generation the prompt type