HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases (2412.16311v2)

Abstract: Given a semi-structured knowledge base (SKB), where text documents are interconnected by relations, how can we effectively retrieve relevant information to answer user questions? Retrieval-Augmented Generation (RAG) retrieves documents to assist LLMs in question answering; while Graph RAG (GRAG) uses structured knowledge bases as its knowledge source. However, many questions require both textual and relational information from SKB - referred to as "hybrid" questions - which complicates the retrieval process and underscores the need for a hybrid retrieval method that leverages both information. In this paper, through our empirical analysis, we identify key insights that show why existing methods may struggle with hybrid question answering (HQA) over SKB. Based on these insights, we propose HybGRAG for HQA consisting of a retriever bank and a critic module, with the following advantages: (1) Agentic, it automatically refines the output by incorporating feedback from the critic module, (2) Adaptive, it solves hybrid questions requiring both textual and relational information with the retriever bank, (3) Interpretable, it justifies decision making with intuitive refinement path, and (4) Effective, it surpasses all baselines on HQA benchmarks. In experiments on the STaRK benchmark, HybGRAG achieves significant performance gains, with an average relative improvement in Hit@1 of 51%.

• The paper introduces HybGRAG, combining textual and graph retrieval to overcome hybrid-sourcing challenges in semi-structured knowledge bases.
• It employs a critic module for iterative refinement that efficiently routes questions over both relational and textual data.
• The system demonstrates a significant 51% improvement in Hit@1 on the STaRK benchmark, setting a new standard for hybrid question answering.

An Evaluation of HybGRAG for Hybrid Question Answering over Semi-Structured Knowledge Bases

The paper "HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases" addresses a nuanced challenge within the field of Retrieval-Augmented Generation (RAG)—specifically, Hybrid Question Answering (HQA) that involves both structured and unstructured data. The researchers introduce a novel approach, HybGRAG, which is designed to enhance the retrieval process in semi-structured knowledge bases (SKBs).

Key Challenges and Contributions

The research identifies two pivotal challenges in HQA over SKBs: the need for a "Hybrid-Sourcing Question" approach that incorporates both relational and textual information, and the requirement for iterative refinement, labeled as a "Refinement-Required Question" due to the initial difficulty of distinguishing the textual from the relational aspects. To address these, HybGRAG combines elements of both existing RAG and Graph RAG (GRAG) systems through the introduction of a retriever bank and a critic module.

Retriever Bank:

HybGRAG's retriever bank is equipped with text and hybrid retrieval modules, enhancing its ability to leverage both kinds of information in SKBs. This solves the identified hybrid-sourcing challenge by dynamically choosing between textual and graph-based data, depending on the question's requirements.

Critic Module:

To tackle the refinement-required challenge, HybGRAG employs a critic module that iteratively improves question routing. By validating and providing feedback on initial retrieval actions, this module refines the extraction process for topic entities and relations.

Performance Evaluation

The paper reports significant performance improvements on the STaRK benchmark, a standard in HQA evaluation. Specifically, HybGRAG achieves a remarkable 51% improvement in Hit@$1$ over its closest competitors, demonstrating robust efficacy and adaptability while remaining interpretable. The enhancements were attributed largely to the system's novel ability to adjust its retrieval strategies dynamically.

Theoretical and Practical Implications

The implementation of HybGRAG has theoretical implications for the design of RAG systems, specifically emphasizing the necessity of hybrid approaches in dealing with SKBs. Practically, its adaptability and interpretability make it a strong candidate for integration into systems that require nuanced understanding and retrieval of complex queries spanning both structured and unstructured data domains.

Future Directions

The insights and methodologies proposed could inform future developments in artificial intelligence, particularly in expanding the capabilities and features of LLMs to operate more effectively with complex information schemas. Further research could explore optimization of the critic module's feedback generation for even more accurate refinement.

In conclusion, this paper makes significant strides in advancing the capabilities of retrieval systems in handling semi-structured data, showcasing an intricate approach to a multifaceted problem by incorporating both retrieval architectures and iterative refinement modules. HybGRAG's success sets a new standard for future research and applications in the field of knowledge retrieval and question answering systems.