Empowering GraphRAG with Knowledge Filtering and Integration

ACL ARR 2025 May Submission3859 Authors

Abstract:

In recent years, large language models (LLMs) have revolutionized the field of natural language processing. However, they often suffer from knowledge gaps and hallucinations. Graph retrieval-augmented generation (GraphRAG) enhances LLM reasoning by integrating structured knowledge from external graphs. However, we identify two key challenges that plague GraphRAG: (1) Retrieving noisy and irrelevant information can degrade performance and (2) Excessive reliance on external knowledge suppresses the model’s intrinsic reasoning.
To address these issues, we propose GraphRAG-FI (Filtering \& Integration), consisting of GraphRAG-Filtering and GraphRAG-Integration. GraphRAG-Filtering employs a two-stage filtering mechanism to refine retrieved information. GraphRAG-Integration employs a logits-based selection strategy to balance external knowledge from GraphRAG with the LLM’s intrinsic reasoning, reducing over-reliance on retrievals.
Experiments on knowledge graph QA tasks demonstrate that GraphRAG-FI significantly improves reasoning performance across multiple backbone models, establishing a more reliable and effective GraphRAG framework.

Paper Type: Long

Research Area: Question Answering

Research Area Keywords: Question Answering, GraphRAG, Knowledge Graph

Languages Studied: English

Submission Number: 3859