用嵌入向量迭代优化查询，大幅降低多跳问答的计算开销。
TreeHop: Efficient Embedding-Level Query Rewriter
- 通过融合历史查询与文档语义，动态更新查询嵌入
- 在4个数据集上性能接近顶尖方法，参数量仅为其2.2%-29.4%
- 适合资源受限或对延迟敏感的部署场景
检索增强生成（RAG）系统在多跳问答（MHQA）中面临挑战，复杂查询需整合多个文档片段信息。现有方法依赖多次大模型查询重写与路由，导致计算成本高昂。为此，我们提出TreeHop，一种无需大模型参与查询优化的嵌入级框架。TreeHop通过融合先前查询与检索文档的语义信息，动态更新查询嵌入，仅通过嵌入空间操作实现迭代检索。该方法将传统“检索-重写-向量化-检索”流程简化为“检索-嵌入-检索”，显著降低计算开销。同时引入基于规则的停止准则，进一步减少冗余检索，在效率与召回率间取得平衡。实验表明，TreeHop在四个开放域多跳问答数据集上表现媲美先进RAG方法，参数量仅为同类方案的2.2%-29.4%，查询延迟降低92.8%-97.8%。代码与数据已开源，便于复现。
原文摘要 · Abstract (English)
Retrieval-augmented generation (RAG) systems face significant challenges in multi-hop question answering (MHQA), where complex queries require synthesizing information across multiple document chunks. Existing approaches typically rely on iterative LLM-based query rewriting and routing, resulting in high computational costs due to repeated LLM invocations and multi-stage processes. To address these limitations, we propose TreeHop, an embedding-level framework without the need for LLMs in query refinement. TreeHop dynamically updates query embeddings by fusing semantic information from prior queries and retrieved documents, enabling iterative retrieval through embedding-space operations alone. This method replaces the traditional "Retrieve-Rewrite-Vectorize-Retrieve" cycle with a streamlined "Retrieve-Embed-Retrieve" loop, significantly reducing computational overhead. Moreover, a rule-based stopping criterion is introduced to further prune redundant retrievals, balancing efficiency and recall rate. Experimental results show that TreeHop rivals advanced RAG methods across four open-domain MHQA datasets, achieving comparable performance with only 2.2\%-29.4\% of the parameter size of concurrent solutions and reducing the query latency by 92.8\%-97.8\%. This makes TreeHop a faster and more cost-effective solution for low-resource or latency-sensitive deployment. For reproducibility purposes, codes and data are available here: https://github.com/allen-li1231/TreeHop-RAG
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