RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery
Journal:
arXiv
Published Date:
Mar 2, 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 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.
