@inproceedings{wang-etal-2025-a3,
    title = "$\mathcal{A}^3$: Automatic Alignment Framework for Attributed Text Generation",
    author = "Wang, Yue  and
      Zhang, Haoke  and
      Li, Juntao  and
      Chang, Jinxiong  and
      Zhang, Min",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1407/",
    pages = "28975--28990",
    ISBN = "979-8-89176-251-0",
    abstract = "Attributed text generation aims to enhance the reliability of content generated from large language models by providing citations for each claim, which thereby enables users to easily verify the correctness of the responses.However, the scarcity of high-quality training samples presents a significant challenge in aligning large language models to generate texts with citations, revealing considerable room for improvement in existing attribution systems.Besides, existing approaches of aligning large language models to follow user instructions can lead to an undue emphasis on irrelevant documents, which in turn reduces the quality of responses.To address the above problems, we propose \textbf{A}utomatic \textbf{A}lignment Framework for \textbf{A}ttributed Text Generation (\textbf{ $\mathcal{A}^3$}), a novel framework designed to automatically generate high-quality attributed query-response pairs for both supervised fine-tuning and preference optimization stages without human annotation.With the help of \textbf{ $\mathcal{A}^3$}, Mistral-7B can achieve a citation recall of \textbf{84.4} and a precision of \textbf{87.0} precision on ASQA, which notably surpasses GPT-4{'}s citation recall of \textbf{73.0} and precision of \textbf{76.5}."
}