Paper Detail
Bao Long Nguyen Huu, Atsushi Hashimoto
GraphRAG is an extension of retrieval-augmented generation (RAG) that supports large language models (LLMs) by referring to graph-structured data as external knowledge. While this technique ideally captures intricate relationships, it often struggles with graph representations for LLMs, particularly for frozen LLMs, due to the misalignment between graph-based and text-based latent features. We tackle this issue by introducing the {\it Adaptive-masking for Graph Embedding (AGE)}. AGE employs a Transformer in a mask-based self-supervised learning (SSL) approach. We designed the architecture similar to text embedding encoders, addressing the latent feature misalignment. In contrast to natural language texts, graphs are concise representations, and there exist {\it key nodes} that hold dominant contextual information, which are challenging to predict from their surroundings. Masking such key nodes leads to inefficiency in the SSL process. Therefore, AGE focuses on predicting nodes apart from key nodes, utilizing a learnable node sampler. Our experimental results indicate that AGE significantly improves approaches using non-parametric search component in GraphQA tasks, achieving superior accuracy across four benchmark datasets with distinct characteristics.
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@misc{huu2026age,
title = {AGE: Adaptive-masking for Graph Embedding in Graph Retrieval-Augmented Generation},
author = {Bao Long Nguyen Huu and Atsushi Hashimoto},
year = {2026},
abstract = {GraphRAG is an extension of retrieval-augmented generation (RAG) that supports large language models (LLMs) by referring to graph-structured data as external knowledge. While this technique ideally captures intricate relationships, it often struggles with graph representations for LLMs, particularly for frozen LLMs, due to the misalignment between graph-based and text-based latent features. We tackle this issue by introducing the \{\textbackslash{}it Adaptive-masking for Graph Embedding (AGE)\}. AGE employs a Tr},
url = {https://huggingface.co/papers/2607.00052},
keywords = {retrieval-augmented generation, large language models, graph-structured data, latent feature misalignment, Transformer, self-supervised learning, GraphQA, key nodes, node sampler, huggingface daily},
eprint = {2607.00052},
archiveprefix = {arXiv},
}
{}