  Multi-hop question answering is a knowledge-intensive complex problem. Large
Language Models (LLMs) use their Chain of Thoughts (CoT) capability to reason
complex problems step by step, and retrieval-augmentation can effectively
alleviate factual errors caused by outdated and unknown knowledge in LLMs.
Recent works have introduced retrieval-augmentation in the CoT reasoning to
solve multi-hop question answering. However, these chain methods have the
following problems: 1) Retrieved irrelevant paragraphs may mislead the
reasoning; 2) An error in the chain structure may lead to a cascade of errors.
  In this paper, we propose a dynamic retrieval framework called Tree of
Reviews (ToR), where the root node is the question, and the other nodes are
paragraphs from retrieval, extending different reasoning paths from the root
node to other nodes. Our framework dynamically decides to initiate a new
search, reject, or accept based on the paragraphs on the reasoning paths.
Compared to related work, we introduce a tree structure to handle each
retrieved paragraph separately, alleviating the misleading effect of irrelevant
paragraphs on the reasoning path; the diversity of reasoning path extension
reduces the impact of a single reasoning error on the whole. We conducted
experiments on three different multi-hop question answering datasets. The
results show that compared to the baseline methods, ToR achieves
state-of-the-art performance in both retrieval and response generation. In
addition, we propose two tree-based search optimization strategies, pruning and
effective expansion, to reduce time overhead and increase the diversity of path
extension. We will release our code.
