Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation
Sirui Xia, Xintao Wang, Jiaqing Liang, Yifei Zhang, Weikang Zhou, Jiaji Deng, Fei Yu, Yanghua Xiao · 2025
Retrieval-Augmented Generation (RAG) has been widely adopted to enhance Large Language Models (LLMs) in knowledge-intensive tasks.To enhance credibility and verifiability in RAG systems, Attributed Text Generation (ATG) is proposed, which provides citations to retrieval knowledge in LLM-generated responses.Prior methods mainly adopt coarsegrained attributions, with passage-level or paragraph-level references or citations, which fall short in verifiability.This paper proposes RECLAIM (Refer & Claim), a fine-grained ATG method that alternates the generation of references and answers step by step.Different from previous coarse-grained attribution, RECLAIM provides sentence-level citations in long-form question-answering tasks.With extensive experiments, we verify the effectiveness of RECLAIM in extensive settings, achieving a citation accuracy rate of 90%. 1