Retrieve-Plan-Generation: An Iterative Planning and Answering Framework for Knowledge-Intensive LLM Generation
Year: 2024
Type: conference-paper
Abstract: Despite the significant progress of large language models (LLMs) in various tasks, they often produce factual errors due to their limited internal knowledge.Retrieval-Augmented Generation (RAG), which enhances LLMs with external knowledge sources, offers a promising solution.However, these methods can be misled by irrelevant paragraphs in retrieved documents.Due to the inherent uncertainty in LLM generation, inputting the entire document may introduce off-topic information, causing the model to deviate from the central topic and affecting the relevance of the generated content.To address these issues, we propose the Retrieve-Plan-Generation (RPG) framework.RPG generates plan tokens to guide subsequent generation in the plan stage.In the answer stage, the model selects relevant fine-grained paragraphs based on the plan and uses them for further answer generation.This plan-answer process is repeated iteratively until completion, enhancing generation relevance by focusing on specific topics.To implement this framework efficiently, we utilize a simple but effective multi-task prompt-tuning method, enabling the existing LLMs to handle both planning and answering.We comprehensively compare RPG with baselines across 5 knowledge-intensive generation tasks, demonstrating the effectiveness of our approach.1
Authors: Yuanjie Lyu, Zihan Niu, Zheyong Xie, Chao Zhang, Tong Xu, Yang Wang, Enhong Chen
Institution: University of Science and Technology of China
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404784206