TreeHop: Generate and Filter Next Query Embeddings Efficiently for
  Multi-hop Question Answering
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.
6811ee5994d2a4531ca2b697
Zhonghao Li
claimed_verified
2025-04-30T12:24:10.370Z
66a378a7a24a4e3865e6aadf
/avatars/ff1560941fad2a6224418f399d38b1cc.svg
Allen Li
user
allen-li1231
6811ee5994d2a4531ca2b698
Kunpeng Zhang
extracted_pending
2025-04-30T09:33:14.657Z
65cb93826427380bc218c408
/avatars/73d834cf4ce7d09d6adb68b1e974cbf8.svg
KZ Zhang
user
kpzhang1028
6811ee5994d2a4531ca2b699
Jinghuai Ou
6811ee5994d2a4531ca2b69a
Shuliang Liu
6811ee5994d2a4531ca2b69b
Xuming Hu
TreeHop: Generate and Filter Next Query Embeddings Efficiently for
  Multi-hop Question Answering
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.
TreeHop, an embedding-level framework for multi-hop question answering, significantly reduces computational costs compared to traditional retrieval-augmented generation systems by eliminating the need for iterative LLM-based query refinement.
retrieval-augmented generation multi-hop question answering query rewriting LLMs TreeHop embedding-level framework query embeddings embedding-space operations rule-based stop criterion open-domain MHQA datasets