StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization
Zhuoqun Li, Xuanang Chen, Haiyang Yu, Hongyu Lin, Yaojie Lu, Qiaoyu Tang, Fei Huang, Xianpei Han, Le Sun, Yongbin Li
2024-10-14
Summary
This paper introduces StructRAG, a new framework designed to improve how large language models (LLMs) handle complex reasoning tasks that require a lot of knowledge by organizing information more effectively.
What's the problem?
Existing methods for enhancing LLMs, like retrieval-augmented generation (RAG), struggle with knowledge-intensive tasks because the important information is often scattered across many sources. This makes it hard for the models to find and connect the necessary details to answer questions accurately.
What's the solution?
StructRAG addresses this issue by using a structured approach to organize information. It identifies the best way to structure the data for each task, reconstructs documents into this format, and then uses the structured information to infer answers. This method mimics how humans organize raw information into structured knowledge when solving complex problems. The framework has been tested across various tasks and has shown to perform better than existing methods.
Why it matters?
This research is significant because it enhances the ability of LLMs to reason with complex knowledge, making them more effective in real-world applications. By improving how these models process and understand information, StructRAG could lead to better performance in areas like education, research, and any field that relies on accurate information retrieval.
Abstract
Retrieval-augmented generation (RAG) is a key means to effectively enhance large language models (LLMs) in many knowledge-based tasks. However, existing RAG methods struggle with knowledge-intensive reasoning tasks, because useful information required to these tasks are badly scattered. This characteristic makes it difficult for existing RAG methods to accurately identify key information and perform global reasoning with such noisy augmentation. In this paper, motivated by the cognitive theories that humans convert raw information into various structured knowledge when tackling knowledge-intensive reasoning, we proposes a new framework, StructRAG, which can identify the optimal structure type for the task at hand, reconstruct original documents into this structured format, and infer answers based on the resulting structure. Extensive experiments across various knowledge-intensive tasks show that StructRAG achieves state-of-the-art performance, particularly excelling in challenging scenarios, demonstrating its potential as an effective solution for enhancing LLMs in complex real-world applications.