Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation
<|reference_start|>Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation: 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.<|reference_end|>
@article{xia2024ground,
    title={Ground Every Sentence: Improving Retrieval-Augmented LLMs with
  Interleaved Reference-Claim Generation},
    author={Sirui Xia, Xintao Wang, Jiaqing Liang, Yifei Zhang, Weikang Zhou,
  Jiaji Deng, Fei Yu, Yanghua Xiao},
    journal={arXiv preprint arXiv:2407.01796},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01796},
    primaryClass={cs.CL}
}