RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866929738500538368 |
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| author | Gu, Hongchao Li, Dexun Dong, Kuicai Zhang, Hao Lv, Hang Wang, Hao Lian, Defu Liu, Yong Chen, Enhong |
| author_facet | Gu, Hongchao Li, Dexun Dong, Kuicai Zhang, Hao Lv, Hang Wang, Hao Lian, Defu Liu, Yong Chen, Enhong |
| contents | 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 direct generation and multi-agent discussion, often struggle with issues like hallucinations, topic incoherence, and significant latency. To address these challenges, we propose RAPID, an efficient retrieval-augmented long text generation framework. 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 (e.g. long-text generation, outline quality, latency, etc). Our work provides a robust and efficient solution to the challenges of automated long-text generation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_00751 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery Gu, Hongchao Li, Dexun Dong, Kuicai Zhang, Hao Lv, Hang Wang, Hao Lian, Defu Liu, Yong Chen, Enhong Computation and Language Artificial Intelligence 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 direct generation and multi-agent discussion, often struggle with issues like hallucinations, topic incoherence, and significant latency. To address these challenges, we propose RAPID, an efficient retrieval-augmented long text generation framework. 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 (e.g. long-text generation, outline quality, latency, etc). Our work provides a robust and efficient solution to the challenges of automated long-text generation. |
| title | RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2503.00751 |