将跨文档推理提前到索引阶段，提升多跳问答效率
IndexRAG: Bridging Facts for Cross-Document Reasoning at Index Time
- 在构建索引时识别文档间的共享实体，生成可独立检索的桥梁事实
- 多跳问答平均提升F1 4.6分，推理仅需单次检索和一次大模型调用
- 无需额外训练，适合追求高效推理的实用系统部署
多跳问答需要跨多个文档进行推理，现有检索增强生成（RAG）方法要么依赖需额外在线处理的图结构方法，要么采用多步迭代推理。我们提出IndexRAG，将跨文档推理从在线推理阶段转移到离线索引阶段。IndexRAG识别跨文档共享的桥接实体，并生成可独立检索的桥梁事实，无需额外训练或微调。在三个常用多跳问答基准（HotpotQA、2WikiMultiHopQA、MuSiQue）上的实验表明，IndexRAG相比朴素RAG平均提升F1 4.6分，且推理时仅需单次检索和一次LLM调用。结合IRCoT后，其性能超过所有图结构基线（包括HippoRAG和FastGraphRAG），同时仅依赖扁平化检索。代码将在论文接受后发布。
原文摘要 · Abstract (English)
Multi-hop question answering (QA) requires reasoning across multiple documents, yet existing retrieval-augmented generation (RAG) approaches address this either through graph-based methods requiring additional online processing or iterative multi-step reasoning. We present IndexRAG, a novel approach that shifts cross-document reasoning from online inference to offline indexing. IndexRAG identifies bridge entities shared across documents and generates bridging facts as independently retrievable units, requiring no additional training or fine-tuning. Experiments on three widely-used multi-hop QA benchmarks (HotpotQA, 2WikiMultiHopQA, MuSiQue) show that IndexRAG improves F1 over Naive RAG by 4.6 points on average, while requiring only single-pass retrieval and a single LLM call at inference time. When combined with IRCoT, IndexRAG outperforms all graph-based baselines on average, including HippoRAG and FastGraphRAG, while relying solely on flat retrieval. Our code will be released upon acceptance.
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