GLM-RAG: Graph Language Models for Graph-Based Retrieval-Augmented Generation
arXiv:2607.28397
The paper introduces a graph language model (GLM) based retriever for retrieval-augmented generation over knowledge graphs and compares it with GNN‑based and vector‑search retrievers, showing that fine‑tuned GLM retrievers generalize better to unseen domains and achieve state‑of‑the‑art performance on multi‑hop benchmarks.
Abstract
Retrieval-augmented generation (RAG) over knowledge graphs requires retrievers that can effectively capture both graph structure and semantic information. Recent approaches have explored graph neural network (GNN)-based retrievers to model graph topology in multi-hop reasoning tasks. In parallel, graph language models (GLMs) have emerged as a promising paradigm that integrates graph reasoning and the semantic capabilities of language models. In this work, we introduce a GLM-based retriever and investigate the comparative strengths of GLM-based, GNN-based, and traditional vector-search-based retrievers in single- and multi-hop RAG settings, and with a particular focus on transferability to unseen domains. Our findings suggest that finetuned GLM retrievers generalize better out of domain, achieving SOTA on two multi-hop benchmarks. On in-domain multi-hop QA datasets they remain comparable to prior work, with promising scaling as parameters and subgraph coverage increase. GNN-based retrievers achieve higher graph coverage with an efficient training setup, whereas the vector-search baseline excels at single-hop datasets.
10 pages, 19 figures