StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization (2410.08815v2)

Abstract: Retrieval-augmented generation (RAG) is a key means to effectively enhance 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.

The paper "StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization" presents an innovative approach to enhancing the reasoning capabilities of LLMs in knowledge-intensive tasks. These tasks are particularly challenging due to the widely dispersed nature of the necessary information across various documents. The authors address these challenges by proposing a novel method that combines retrieval-augmented generation (RAG) techniques with a structured transformation of the retrieved information at inference time.

Key Concepts and Framework

The core idea behind StructRAG is inspired by cognitive theories which suggest that, similar to human reasoning processes, information should be structured in specific formats to facilitate effective comprehension and reasoning. The framework introduces three interconnected modules:

1. Hybrid Structure Router: This component is responsible for selecting the appropriate structure type based on the specific requirements of a task. The selection is informed by a training approach that includes task synthesis, solution simulation, and preference judgment, leveraging a newly proposed training method incorporating preference data. The decision-making process is underpinned by the DPO algorithm to align structure choices with the task demands.
2. Scattered Knowledge Structurizer: Once the optimal structure is selected, this module transforms the raw information from documents into the chosen structured format. Formats may include tables, graphs, algorithms, catalogues, or text chunks, each suited to different task types. This transformation process utilizes the advanced comprehension and generation capabilities of LLMs.
3. Structured Knowledge Utilizer: This module decomposes complex questions into simpler sub-questions, enhancing the precision of information extraction and reasoning. By working with structured knowledge, it facilitates effective retrieval and generation outcomes even in complex information environments.

Experimental Validation

Extensive experiments highlight StructRAG's performance across a range of knowledge-intensive reasoning tasks. It notably outperformed several baseline methods, including Long-Context, traditional RAG, RQ-RAG, and GraphRAG. The system's most significant improvements were observed in scenarios where information was highly dispersed within the documents, showcasing the efficacy of using structured knowledge to support reasoning.

Implications and Future Work

The implications of this work extend to various real-world applications that require sophisticated information processing, such as financial analysis or long-chain reasoning. The framework's ability to dynamically tailor information structures according to task-specific needs reflects a significant advancement towards integrating human-like cognitive processes into AI systems.

Future directions for this research could involve refining the structure selection process to further adapt it to diverse task types, expanding the range of supported information structures, and enhancing the robustness of the overall information structuring and utilization techniques. Exploring these directions could continue to bridge the gap between human reasoning and machine processing, leading to more intuitive and effective AI-driven decision-making capabilities.