TreeHop: Generate and Filter Next Query Embeddings Efficiently for Multi-hop Question Answering

• URL: http://arxiv.org/abs/2504.20114v2
• Date: Wed, 30 Apr 2025 13:15:49 GMT
• Title: TreeHop: Generate and Filter Next Query Embeddings Efficiently for Multi-hop Question Answering
• Authors: Zhonghao Li, Kunpeng Zhang, Jinghuai Ou, Shuliang Liu, Xuming Hu,
• Abstract summary: TreeHop is an embedding-level framework for multi-hop question answering.<n>TreeHop dynamically updates query embeddings by fusing semantic information from prior queries.<n>TreeHop is a faster and more cost-effective solution for deployment in a range of knowledge-intensive applications.
• Score: 27.37434534716611
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 stop criterion is introduced to further prune redundant retrievals, balancing efficiency and recall rate. Experimental results show that TreeHop rivals advanced RAG methods across three open-domain MHQA datasets, achieving comparable performance with only 5\%-0.4\% of the model parameter size and reducing the query latency by approximately 99\% compared to concurrent approaches. This makes TreeHop a faster and more cost-effective solution for deployment in a range of knowledge-intensive applications. For reproducibility purposes, codes and data are available here: https://github.com/allen-li1231/TreeHop-RAG.

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