GLM-RAG: Graph Language Models for Graph-Based Retrieval-Augmented Generation

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.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)

I.2.0; I.2.4; I.2.7

arXiv:2607.28397 [cs.AI]

(or arXiv:2607.28397v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.28397 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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