Leveraging external knowledge to enhance large language models is a crucial challenge. Retrieval-augmented generation methods are promising solutions that provide relevant information from external sources to improve factual accuracy and reasoning capabilities. However, existing approaches struggle with knowledge-intensive reasoning tasks where information is dispersed across documents, hindering accurate integration for global reasoning.
The StructRAG framework, inspired by cognitive theories, introduces a hybrid structurization mechanism to enhance LLM performance on knowledge-intensive reasoning tasks. By mimicking human-like thinking processes, StructRAG aims to improve reasoning capabilities by constructing structured knowledge based on task requirements.
In this article, we explore StructRAG’s components, training the hybrid structure router, and experimental results demonstrating its effectiveness. The framework consists of a structure router, scattered knowledge structurizer, and structured knowledge utilizer, working together for accurate reasoning.
Structured knowledge is essential for enhancing large language models on complex reasoning tasks. With the StructRAG framework, a new approach is introduced to improve reasoning accuracy by leveraging structured knowledge in the optimal format.
Let us delve deeper into the key modules of the StructRAG framework and understand how they work together to enhance performance on knowledge-intensive tasks.
- Hybrid Structure Router: This core component determines the most suitable structure type based on the input. By considering different structure types, the router enhances the effectiveness of subsequent modules.
The training process of the hybrid structure router utilizes a novel method that focuses on reinforcement learning principles without the need for additional reward models. By generating high-quality synthetic preference pairs, the router learns to select the optimal structure type for various tasks.
Scattered Knowledge Structurizer: This module extracts relevant information from raw documents and transforms it into structured knowledge. Leveraging LLM capabilities, it reconstructs information into a structured format suitable for the task.
Structured Knowledge Utilizer: The final module focuses on reasoning based on the constructed structured knowledge. By decomposing questions, extracting precise knowledge, and inferring answers, this module enhances reasoning and accuracy.