@inproceedings{gu-etal-2025-rapid,
    title = "{RAPID}: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery",
    author = "Gu, Hongchao  and
      Li, Dexun  and
      Dong, Kuicai  and
      Zhang, Hao  and
      Lv, Hang  and
      Wang, Hao  and
      Lian, Defu  and
      Liu, Yong  and
      Chen, Enhong",
    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://preview.aclanthology.org/display_plenaries/2025.findings-acl.859/",
    pages = "16742--16763",
    ISBN = "979-8-89176-256-5",
    abstract = "Generating knowledge-intensive and comprehensive long texts, such as encyclopedia articles, remains significant challenges for Large Language Models. It requires not only the precise integration of facts but also the maintenance of thematic coherence throughout the article. Existing methods, such as multi-agent discussion, often struggle with issues like hallucinations, topic incoherence, and significant latency. To address these challenges, we propose RAPID, an efficient **R**etrieval-**A**ugmented long text generation framework with writing **P**lanning and **I**nformation **D**iscovery. RAPID consists of three main modules: (1) Retrieval-augmented preliminary outline generation to reduce hallucinations, (2) Attribute-constrained search for efficient information discovery, (3) Plan-guided article generation for enhanced coherence. Extensive experiments on our newly compiled benchmark dataset, FreshWiki-2024, demonstrate that RAPID significantly outperforms state-of-the-art methods across a wide range of evaluation metrics (long-text generation, outline quality, latency, etc). Our work provides a robust and efficient solution to the challenges of automated long-text generation."
}