RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery

• URL: http://arxiv.org/abs/2503.00751v1
• Date: Sun, 02 Mar 2025 06:11:29 GMT
• Title: RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery
• Authors: Hongchao Gu, Dexun Li, Kuicai Dong, Hao Zhang, Hang Lv, Hao Wang, Defu Lian, Yong Liu, Enhong Chen,
• Abstract summary: Existing methods, such as direct generation and multi-agent discussion, often struggle with issues like hallucinations, topic incoherence, and significant latency.<n>We propose RAPID, an efficient retrieval-augmented long text generation framework.<n>Our work provides a robust and efficient solution to the challenges of automated long-text generation.
• Score: 69.41989381702858
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

Related papers

• DeFine: A Decomposed and Fine-Grained Annotated Dataset for Long-form Article Generation [24.091769825963173]
  We introduce DeFine, a Decomposed and Fine-grained annotated dataset for long-form article generation. DeFine is characterized by its hierarchical decomposition strategy and the integration of domain-specific knowledge with multi-level annotations. The experimental results showed significant improvements in text quality, specifically in topic coverage, depth of information, and content fidelity.
  arXiv  Detail & Related papers  (2025-03-10T10:48:00Z)
• A LongFormer-Based Framework for Accurate and Efficient Medical Text Summarization [3.4635278365524673]
  This paper proposes a medical text summarization method based on LongFormer. Traditional summarization methods are often limited by short-term memory. LongFormer effectively captures long-range dependencies in the text, retaining more key information.
  arXiv  Detail & Related papers  (2025-03-10T03:33:45Z)
• FACT: Examining the Effectiveness of Iterative Context Rewriting for Multi-fact Retrieval [20.217386507637475]
  Large Language Models (LLMs) are proficient at retrieving single facts from extended contexts, yet struggle with tasks requiring the simultaneous retrieval of multiple facts. This paper identifies a novel "lost-in-the-middle" phenomenon, where LLMs progressively lose track of critical information throughout the generation process. We introduce Find All Crucial Texts (FACT), an iterative retrieval method that refines context through successive rounds of rewriting.
  arXiv  Detail & Related papers  (2024-10-28T13:36:41Z)
• Integrating Planning into Single-Turn Long-Form Text Generation [66.08871753377055]
  We propose to use planning to generate long form content. Our main novelty lies in a single auxiliary task that does not require multiple rounds of prompting or planning. Our experiments demonstrate on two datasets from different domains, that LLMs fine-tuned with the auxiliary task generate higher quality documents.
  arXiv  Detail & Related papers  (2024-10-08T17:02:40Z)
• Analysis of Plan-based Retrieval for Grounded Text Generation [78.89478272104739]
  hallucinations occur when a language model is given a generation task outside its parametric knowledge. A common strategy to address this limitation is to infuse the language models with retrieval mechanisms. We analyze how planning can be used to guide retrieval to further reduce the frequency of hallucinations.
  arXiv  Detail & Related papers  (2024-08-20T02:19:35Z)
• QAEA-DR: A Unified Text Augmentation Framework for Dense Retrieval [11.62210546106209]
  In dense retrieval, embedding long texts into dense vectors can result in information loss, leading to inaccurate query-text matching.<n>Recent studies mainly focus on improving the sentence embedding model or retrieval process.<n>We introduce a novel text augmentation framework for dense retrieval, which transforms raw documents into information-dense text formats.
  arXiv  Detail & Related papers  (2024-07-29T17:39:08Z)
• CorpusLM: Towards a Unified Language Model on Corpus for Knowledge-Intensive Tasks [20.390672895839757]
  Retrieval-augmented generation (RAG) has emerged as a popular solution to enhance factual accuracy. Traditional retrieval modules often rely on large document index and disconnect with generative tasks. We propose textbfCorpusLM, a unified language model that integrates generative retrieval, closed-book generation, and RAG.
  arXiv  Detail & Related papers  (2024-02-02T06:44:22Z)
• FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios [87.12753459582116]
  A wider range of tasks now face an increasing risk of containing factual errors when handled by generative models. We propose FacTool, a task and domain agnostic framework for detecting factual errors of texts generated by large language models.
  arXiv  Detail & Related papers  (2023-07-25T14:20:51Z)
• Deliberate then Generate: Enhanced Prompting Framework for Text Generation [70.10319005141888]
  Deliberate then Generate (DTG) prompting framework consists of error detection instructions and candidates that may contain errors. We conduct extensive experiments on 20+ datasets across 7 text generation tasks, including summarization, translation, dialogue, and more. We show that DTG consistently outperforms existing prompting methods and achieves state-of-the-art performance on multiple text generation tasks.
  arXiv  Detail & Related papers  (2023-05-31T13:23:04Z)
• Partially-Aligned Data-to-Text Generation with Distant Supervision [69.15410325679635]
  We propose a new generation task called Partially-Aligned Data-to-Text Generation (PADTG) It is more practical since it utilizes automatically annotated data for training and thus considerably expands the application domains. Our framework outperforms all baseline models as well as verify the feasibility of utilizing partially-aligned data.
  arXiv  Detail & Related papers  (2020-10-03T03:18:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.