Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinking

• URL: http://arxiv.org/abs/2508.02435v1
• Date: Mon, 04 Aug 2025 13:50:44 GMT
• Title: Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinking
• Authors: Shengbo Gong, Xianfeng Tang, Carl Yang, Wei jin,
• Abstract summary: Retrieval-augmented generation (RAG) is critical for reducing hallucinations and incorporating external knowledge into Large Language Models (LLMs)<n>We propose T$2$RAG, a novel framework that operates on a simple, graph-free knowledge base of atomic triplets.<n> Empirical results show that T$2$RAG significantly outperforms state-of-the-art multi-round and Graph RAG methods.
• Score: 31.73448933991891
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-augmented generation (RAG) is critical for reducing hallucinations and incorporating external knowledge into Large Language Models (LLMs). However, advanced RAG systems face a trade-off between performance and efficiency. Multi-round RAG approaches achieve strong reasoning but incur excessive LLM calls and token costs, while Graph RAG methods suffer from computationally expensive, error-prone graph construction and retrieval redundancy. To address these challenges, we propose T$^2$RAG, a novel framework that operates on a simple, graph-free knowledge base of atomic triplets. T$^2$RAG leverages an LLM to decompose questions into searchable triplets with placeholders, which it then iteratively resolves by retrieving evidence from the triplet database. Empirical results show that T$^2$RAG significantly outperforms state-of-the-art multi-round and Graph RAG methods, achieving an average performance gain of up to 11\% across six datasets while reducing retrieval costs by up to 45\%. Our code is available at https://github.com/rockcor/T2RAG

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