Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation

• URL: http://arxiv.org/abs/2407.01796v2
• Date: Fri, 23 May 2025 04:38:57 GMT
• Title: Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation
• Authors: Sirui Xia, Xintao Wang, Jiaqing Liang, Yifei Zhang, Weikang Zhou, Jiaji Deng, Fei Yu, Yanghua Xiao,
• Abstract summary: Attributed Text Generation (ATG) is proposed to enhance credibility and verifiability in RAG systems.<n>This paper proposes ReClaim, a fine-grained ATG method that alternates the generation of references and answers step by step.<n>With extensive experiments, we verify the effectiveness of ReClaim in extensive settings, achieving a citation accuracy rate of 90%.
• Score: 51.8188846284153
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 coarse-grained 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%.

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