RAPID : Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery (2025)

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.

## introduction large language models (llms) have showcased significant proficiency in handling various natural language tasks @xcite @xcite , achieving near-human performance in tasks like summarizing lengthy documents @xcite and crafting poetry @xcite . despite these achievements, generating long and knowledge-intensive texts (e.g., encyclopedia articles) faces significant challenge @xcite . such tasks demand not only the seamless integration of facts and narratives over exten-the lk-99 typically features 6 to 7 degrees of freedom, … i want to write an article about lk-99. ## methods in this section, we introduce rapid, an efficient and effective framework for automated wiki-style article generation. we first formulate the article generation problem in section 3.1. next, we detail the retrieval-augmented outline generation process in section 3.2, the attribute-constrained search in section 3.3, and plan-guided long-text generation in section 3.4. the overall framework of rapid is illustrated in figure 2 . ## human evaluation to gain deeper insights into the quality of the generated articles, we recruited 10 master-degree level volunteers to conduct a human evaluation. we randomly selected 20 topics and requested the volunteers to evaluate articles generated by both rapid and storm for each topic. each article was assigned to two different volunteers to ensure a balanced and unbiased assessment. we as shown in notably, the significantly lower relevance score of storm may be attributed to hallucination propagation caused by its agent-based discussion mechanism. for example, feedback from volunteers revealed that when processing the topic vultures 1 (album), storm's pre-writing phase-relying solely on llm-driven dialogue for information collection-misinterpreted the task, mistakenly assuming it involved generating content about vultures (the animal). this misunderstanding led to irrelevant, animal-related perspectives, rendering the entire information collection and writing process ineffective. in contrast, rapid addresses this issue by employing retrieval-based methods to ensure accurate intent recognition from the outset. this approach enables a more precise and efficient information collection process, significantly reducing the risk of misinterpretation and ultimately improving the quality of the generated content. ## conclusion in this paper, we addressed the challenge of knowledge-intensive long-text generation, with a particular focus on encyclopedia-style article generation. we introduced rapid, an efficient and effective retrieval-augmented writing system that integrates a structured writing plan and information discovery mechanisms. our extensive experiments and human evaluations demonstrate that our model outperforms state-of-the-art baselines in both effectiveness and efficiency. additionally, our model maintains scalability for other knowledge-intensive long-text writing tasks. in the future, we plan to explore the potential of llms in other domainspecific writing tasks, such as financial reports.