@inproceedings{xia-etal-2025-ground,
    title = "Ground Every Sentence: Improving Retrieval-Augmented {LLM}s with Interleaved Reference-Claim Generation",
    author = "Xia, Sirui  and
      Wang, Xintao  and
      Liang, Jiaqing  and
      Zhang, Yifei  and
      Zhou, Weikang  and
      Deng, Jiaji  and
      Yu, Fei  and
      Xiao, Yanghua",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.55/",
    pages = "969--988",
    ISBN = "979-8-89176-195-7",
    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{\%}."
}