让大模型在复杂任务中更准推理，通过动态构建信息结构。
StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization
- 根据任务自动选择最优结构，重构文档为可推理格式。
- 在多个知识密集型任务上达顶尖性能，尤其擅长挑战场景。
- 适合需要深度理解与全局推理的高阶应用，如医疗、法律。
检索增强生成（RAG）是提升大语言模型在知识型任务中表现的关键方法。然而，现有RAG方法在知识密集型推理任务中表现不佳，因为所需信息通常分散且杂乱，难以准确识别关键内容并进行全局推理。受人类认知理论启发——人在处理此类任务时会将原始信息转化为多种结构化知识，本文提出新框架StructRAG：能自动识别当前任务的最佳结构类型，将原始文档重构为该结构，并基于结构化结果进行推理。在多个知识密集型任务上的广泛实验表明，StructRAG达到当前最佳性能，尤其在高难度场景下表现突出，展现出其在复杂真实应用场景中增强大模型能力的巨大潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。