Large language models (LLMs) are capable of answering knowledge-intensive
complex questions with chain-of-thought (CoT) reasoning. However, they tend to
generate factually incorrect reasoning steps when the required knowledge is not
available or up-to-date in models' parameters. Recent works turn to retrieving
external knowledge to augment CoT reasoning. Despite being promising, these
chain-based methods suffer from: 1) Negative retrieval. Unnecessary or
incorrect retrieval may mislead the reasoning; 2) Limited sight. Lacking the
ability to look backward or forward, a local error in one step will propagate
along the chain.
In this paper, we propose a novel approach: Probabilistic Tree-of-thought
Reasoning (ProbTree). First, LLMs translate a complex question into a query
tree, in which each non-root node denotes a sub-question of its parent node.
Then, probabilistic reasoning is conducted over the tree, by solving questions
from leaf to root considering the confidence of both question decomposing and
answering. During reasoning, for leaf nodes, LLMs choose a more confident
answer from Closed-book QA that employs parametric knowledge and Open-book QA
that employs retrieved external knowledge, thus eliminating the negative
retrieval problem. For non-leaf nodes, with the hierarchical structure, LLMs
have broader sights and are able to globally reason with the information from
child nodes, thus recovering from local errors. The experiments on three
Complex QA datasets under the open-domain setting show that our approach
outperforms SOTA methods significantly, demonstrating the effect of
probabilistic tree-of-thought reasoning.

大型语言模型（LLMs）能够用链式思维推理回答知识密集型复杂问题，但当模型的参数中缺少所需的知识或不是最新的时，它们往往会产生错误的推理步骤。本文提出一种新方法：概率思维树推理（ProbTree）。在开放领域的情境下，我们利用外部知识检索加强链式推理，通过解决从叶节点到根节点的问题，考虑问题分解和回答的置信度，在推理过程中消除了负面检索问题，并借助层次结构对非叶节点进行全局推理，从而弥补了局部错误。实验证明，我们的方法在三个复杂问答数据集上显著优于最先进方法，证明了概率思维树推理的有效性。

回答知识密集型复杂问题的概率思维推理

Probabilistic Tree-of-thought Reasoning for Answering  Knowledge-intensive Complex Questions

Neural network models are capable of generating extremely natural sounding
conversational interactions. Nevertheless, these models have yet to demonstrate
that they can incorporate content in the form of factual information or
entity-grounded opinion that would enable them to serve in more task-oriented
conversational applications. This paper presents a novel, fully data-driven,
and knowledge-grounded neural conversation model aimed at producing more
contentful responses without slot filling. We generalize the widely-used
Seq2Seq approach by conditioning responses on both conversation history and
external "facts", allowing the model to be versatile and applicable in an
open-domain setting. Our approach yields significant improvements over a
competitive Seq2Seq baseline. Human judges found that our outputs are
significantly more informative.

本文针对神经网络模型生成内容不足的对话应用展开了研究，提出了一种全新的、基于数据和知识驱动的神经对话模型，该模型通过同时考虑历史对话和外部 “事实” 来生成更有用的回应，实验证明该方法在开放领域中具有广泛应用前景，相较于基线模型的 Seq2Seq 方法，能够显著提高输出的信息量。