  Retrieval-Augmented Generation (RAG) has been widely adopted to enhance Large
Language Models (LLMs) in knowledge-intensive tasks. Recently, Attributed Text
Generation (ATG) has attracted growing attention, which provides citations to
support the model's responses in RAG, so as to enhance the credibility of
LLM-generated content and facilitate verification. Prior methods mainly adopt
coarse-grained attributions, linking to passage-level references or providing
paragraph-level citations. However, these methods still fall short in
verifiability and require certain time costs for fact checking. This paper
proposes a fine-grained ATG method called ReClaim(Refer & Claim), which
alternates the generation of references and answers step by step. Unlike
traditional coarse-grained attribution, ReClaim allows the model to add
sentence-level fine-grained citations to each answer sentence in long-form
question-answering tasks. Our experiments encompass various training and
inference methods and multiple LLMs, verifying the effectiveness of our
approach.
