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

paperactiveprovisionalglm-rag-graph-language-models-for-graph-based-retrieval-augmented-generation-7f17b928·1 events·first seen 1h ago

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

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GLM-RAG

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5arXiv · cs.CL·1h ago·source ↗

GLM-RAG: Graph Language Model retrievers outperform GNNs and vector search on out-of-domain multi-hop reasoning

A new arXiv preprint introduces GLM-RAG, a retrieval-augmented generation framework using graph language model (GLM)-based retrievers over knowledge graphs. The authors compare GLM-based, GNN-based, and vector-search retrievers across single- and multi-hop QA settings, finding that finetuned GLM retrievers generalize better to unseen domains and achieve state-of-the-art on two multi-hop benchmarks. GNN-based retrievers offer better graph coverage with efficient training, while vector search excels at single-hop tasks. The work provides a systematic comparison of retriever architectures for knowledge-graph RAG with practical implications for domain transfer.

Evaluation and Benchmarking Agent and Tool Ecosystem GLM-RAG GLM-RAG: Graph Language Models for Graph-Based Retrieval-Augmented Generation