@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://aclanthology.org/2025.findings-naacl.55/",
doi = "10.18653/v1/2025.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{\%}."
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%0 Conference Proceedings
%T Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation
%A Xia, Sirui
%A Wang, Xintao
%A Liang, Jiaqing
%A Zhang, Yifei
%A Zhou, Weikang
%A Deng, Jiaji
%A Yu, Fei
%A Xiao, Yanghua
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F xia-etal-2025-ground
%X 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%.
%R 10.18653/v1/2025.findings-naacl.55
%U https://aclanthology.org/2025.findings-naacl.55/
%U https://doi.org/10.18653/v1/2025.findings-naacl.55
%P 969-988
Markdown (Informal)
[Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation](https://aclanthology.org/2025.findings-naacl.55/) (Xia et al., Findings 2025)
ACL
- Sirui Xia, Xintao Wang, Jiaqing Liang, Yifei Zhang, Weikang Zhou, Jiaji Deng, Fei Yu, and Yanghua Xiao. 2025. Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 969–988, Albuquerque, New Mexico. Association for Computational Linguistics.