GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation

TL;DR

GFM-RAG introduces a graph foundation model that enhances retrieval-augmented generation by reasoning over complex knowledge graphs, achieving state-of-the-art results without fine-tuning across diverse datasets.

Contribution

The paper presents GFM-RAG, a novel graph neural network-based model that captures complex knowledge relationships and generalizes to unseen datasets without fine-tuning.

Findings

Achieves state-of-the-art performance on multi-hop QA datasets.

Effectively models complex knowledge relationships with a graph neural network.

Maintains efficiency and aligns with neural scaling laws.

Abstract

Retrieval-augmented generation (RAG) has proven effective in integrating knowledge into large language models (LLMs). However, conventional RAGs struggle to capture complex relationships between pieces of knowledge, limiting their performance in intricate reasoning that requires integrating knowledge from multiple sources. Recently, graph-enhanced retrieval augmented generation (GraphRAG) builds graph structure to explicitly model these relationships, enabling more effective and efficient retrievers. Nevertheless, its performance is still hindered by the noise and incompleteness within the graph structure. To address this, we introduce GFM-RAG, a novel graph foundation model (GFM) for retrieval augmented generation. GFM-RAG is powered by an innovative graph neural network that reasons over graph structure to capture complex query-knowledge relationships. The GFM with 8M parameters undergoes a two-stage training process on large-scale datasets, comprising 60 knowledge graphs with over 14M triples and 700k documents. This results in impressive performance and generalizability for GFM-RAG, making it the first graph foundation model applicable to unseen datasets for retrieval without any fine-tuning required. Extensive experiments on three multi-hop QA datasets and seven domain-specific RAG datasets demonstrate that GFM-RAG achieves state-of-the-art performance while maintaining efficiency and alignment with neural scaling laws, highlighting its potential for further improvement.