@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://aclanthology.org/2025.findings-acl.859/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery
%A Gu, Hongchao
%A Li, Dexun
%A Dong, Kuicai
%A Zhang, Hao
%A Lv, Hang
%A Wang, Hao
%A Lian, Defu
%A Liu, Yong
%A Chen, Enhong
%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 gu-etal-2025-rapid
%X 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.
%R 10.18653/v1/2025.findings-acl.859
%U https://aclanthology.org/2025.findings-acl.859/
%U https://doi.org/10.18653/v1/2025.findings-acl.859
%P 16742-16763
Markdown (Informal)
[RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery](https://aclanthology.org/2025.findings-acl.859/) (Gu et al., Findings 2025)
ACL
- Hongchao Gu, Dexun Li, Kuicai Dong, Hao Zhang, Hang Lv, Hao Wang, Defu Lian, Yong Liu, and Enhong Chen. 2025. RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery. In Findings of the Association for Computational Linguistics: ACL 2025, pages 16742–16763, Vienna, Austria. Association for Computational Linguistics.