TreeHop: Generate and Filter Next Query Embeddings Efficiently for
  Multi-hop Question Answering
Authors: name: Zhonghao Li, name: Kunpeng Zhang, name: Jinghuai Ou, name: Shuliang Liu, name: Xuming Hu

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.