TERAG: Token-Efficient Graph-Based Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2509.18667v3
• Date: Mon, 10 Nov 2025 14:28:10 GMT
• Title: TERAG: Token-Efficient Graph-Based Retrieval-Augmented Generation
• Authors: Qiao Xiao, Hong Ting Tsang, Jiaxin Bai,
• Abstract summary: We propose TERAG, a framework designed to build informative graphs at a significantly lower cost.<n>Inspired by HippoRAG, we incorporate Personalized PageRank (PPR) during the retrieval phase.<n>We achieve at least 80% of the accuracy of widely used graph-based RAG methods while consuming only 3%-11% of the output tokens.
• Score: 15.606880864583436
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph-based Retrieval-augmented generation (RAG) has become a widely studied approach for improving the reasoning, accuracy, and factuality of Large Language Models (LLMs). However, many existing graph-based RAG systems overlook the high cost associated with LLM token usage during graph construction, hindering large-scale adoption. To address this, we propose TERAG, a simple yet effective framework designed to build informative graphs at a significantly lower cost. Inspired by HippoRAG, we incorporate Personalized PageRank (PPR) during the retrieval phase, and we achieve at least 80% of the accuracy of widely used graph-based RAG methods while consuming only 3%-11% of the output tokens. With its low token footprint and efficient construction pipeline, TERAG is well-suited for large-scale and cost-sensitive deployment scenarios.

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