@inproceedings{zhang-etal-2025-longcite,
title = "{L}ong{C}ite: Enabling {LLM}s to Generate Fine-grained Citations in Long-Context {QA}",
author = "Zhang, Jiajie and
Bai, Yushi and
Lv, Xin and
Gu, Wanjun and
Liu, Danqing and
Zou, Minhao and
Cao, Shulin and
Hou, Lei and
Dong, Yuxiao and
Feng, Ling and
Li, Juanzi",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.264/",
doi = "10.18653/v1/2025.findings-acl.264",
pages = "5098--5122",
ISBN = "979-8-89176-256-5",
abstract = "Though current long-context large language models (LLMs) have demonstrated impressive capacities in answering various questions based on extensive text, the lack of citations in their responses makes user verification difficult, leading to concerns about their trustworthiness due to the potential hallucinations. In this work, we aim to enable long-context LLMs to generate responses with fine-grained sentence-level citations on the fly, thereby improving their faithfulness and verifiability. We first introduce LongBench-Cite, an automated benchmark for assessing current LLMs' performance in long-context question answering with citations (LQAC), revealing considerable room for improvement. To this end, we propose CoF (Coarse to Fine), a novel pipeline that utilizes off-the-shelf LLMs to automatically construct long-context QA instances with precise sentence-level citations, and leverage this pipeline to construct LongCite-45k, a large-scale SFT dataset for LQAC. Finally, we train LongCite-8B and LongCite-9B using the constructed dataset, successfully enabling the generation of accurate responses and fine-grained citations in one pass. The evaluation results on LongBench-Cite show that our trained models achieve state-of-the-art citation quality, surpassing advanced proprietary models including GPT-4o. We also discover that SFT with citation information can further improve the correctness of model responses compared to standard long-context SFT."
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<abstract>Though current long-context large language models (LLMs) have demonstrated impressive capacities in answering various questions based on extensive text, the lack of citations in their responses makes user verification difficult, leading to concerns about their trustworthiness due to the potential hallucinations. In this work, we aim to enable long-context LLMs to generate responses with fine-grained sentence-level citations on the fly, thereby improving their faithfulness and verifiability. We first introduce LongBench-Cite, an automated benchmark for assessing current LLMs’ performance in long-context question answering with citations (LQAC), revealing considerable room for improvement. To this end, we propose CoF (Coarse to Fine), a novel pipeline that utilizes off-the-shelf LLMs to automatically construct long-context QA instances with precise sentence-level citations, and leverage this pipeline to construct LongCite-45k, a large-scale SFT dataset for LQAC. Finally, we train LongCite-8B and LongCite-9B using the constructed dataset, successfully enabling the generation of accurate responses and fine-grained citations in one pass. The evaluation results on LongBench-Cite show that our trained models achieve state-of-the-art citation quality, surpassing advanced proprietary models including GPT-4o. We also discover that SFT with citation information can further improve the correctness of model responses compared to standard long-context SFT.</abstract>
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%0 Conference Proceedings
%T LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-Context QA
%A Zhang, Jiajie
%A Bai, Yushi
%A Lv, Xin
%A Gu, Wanjun
%A Liu, Danqing
%A Zou, Minhao
%A Cao, Shulin
%A Hou, Lei
%A Dong, Yuxiao
%A Feng, Ling
%A Li, Juanzi
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F zhang-etal-2025-longcite
%X Though current long-context large language models (LLMs) have demonstrated impressive capacities in answering various questions based on extensive text, the lack of citations in their responses makes user verification difficult, leading to concerns about their trustworthiness due to the potential hallucinations. In this work, we aim to enable long-context LLMs to generate responses with fine-grained sentence-level citations on the fly, thereby improving their faithfulness and verifiability. We first introduce LongBench-Cite, an automated benchmark for assessing current LLMs’ performance in long-context question answering with citations (LQAC), revealing considerable room for improvement. To this end, we propose CoF (Coarse to Fine), a novel pipeline that utilizes off-the-shelf LLMs to automatically construct long-context QA instances with precise sentence-level citations, and leverage this pipeline to construct LongCite-45k, a large-scale SFT dataset for LQAC. Finally, we train LongCite-8B and LongCite-9B using the constructed dataset, successfully enabling the generation of accurate responses and fine-grained citations in one pass. The evaluation results on LongBench-Cite show that our trained models achieve state-of-the-art citation quality, surpassing advanced proprietary models including GPT-4o. We also discover that SFT with citation information can further improve the correctness of model responses compared to standard long-context SFT.
%R 10.18653/v1/2025.findings-acl.264
%U https://aclanthology.org/2025.findings-acl.264/
%U https://doi.org/10.18653/v1/2025.findings-acl.264
%P 5098-5122
Markdown (Informal)
[LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-Context QA](https://aclanthology.org/2025.findings-acl.264/) (Zhang et al., Findings 2025)
ACL
- Jiajie Zhang, Yushi Bai, Xin Lv, Wanjun Gu, Danqing Liu, Minhao Zou, Shulin Cao, Lei Hou, Yuxiao Dong, Ling Feng, and Juanzi Li. 2025. LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-Context QA. In Findings of the Association for Computational Linguistics: ACL 2025, pages 5098–5122, Vienna, Austria. Association for Computational Linguistics.