Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinking
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909720777850880 |
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| author | Gong, Shengbo Tang, Xianfeng Yang, Carl jin, Wei |
| author_facet | Gong, Shengbo Tang, Xianfeng Yang, Carl jin, Wei |
| contents | 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 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_02435 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinking Gong, Shengbo Tang, Xianfeng Yang, Carl jin, Wei Information Retrieval H.3 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 |
| title | Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinking |
| topic | Information Retrieval H.3 |
| url | https://arxiv.org/abs/2508.02435 |