用原子三元组替代图结构，提升检索生成效率与准确率
Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinking
- 将问题拆解为带占位符的可检索三元组，逐轮求解
- 在6个数据集上平均性能提升11%，检索成本降低45%
- 适合需要高效精准外部知识融合的场景
检索增强生成（RAG）对减少大语言模型幻觉、引入外部知识至关重要。但先进RAG系统面临性能与效率的权衡：多轮RAG虽推理能力强，但调用次数和令牌开销过大；图RAG方法则因计算昂贵、易出错的图构建及检索冗余而受限。为此，我们提出T²RAG，一种基于原子三元组的简单无图知识库框架。T²RAG利用大语言模型将问题分解为含占位符的可搜索三元组，并通过迭代从三元组数据库中检索证据来逐步求解。实证结果表明，T²RAG显著优于现有先进多轮与图RAG方法，在六个数据集上平均性能提升最高达11%，同时检索成本降低最多45%。代码已开源。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。