bibtext: @inproceedings{
  zhu2024deductive,
  title={Deductive Beam Search: Decoding Deducible Rationale for Chain-of-Thought Reasoning},
  author={Tinghui Zhu and Kai Zhang and Jian Xie and Yu Su},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=S1XnUsqwr7}
  }
abstract: Recent advancements have significantly augmented the reasoning capabilities of Large Language Models (LLMs) through various methodologies, especially chain-of-thought (CoT) reasoning. However, previous methods often struggle to address reasoning errors in intermediate steps, which can lead to accumulative errors. In this paper, we propose Deductive Beam Search (DBS), which seamlessly integrates CoT and deductive reasoning with step-wise beam search for LLMs. Our approach deploys a verifier, verifying the deducibility of a reasoning step and its premises, thus alleviating the error accumulation. Furthermore, we introduce a scalable and labor-free data construction method to amplify our model’s verification capabilities. Extensive experiments demonstrate that our approach significantly enhances the base performance of LLMs of various scales (7B, 13B, 70B, and ChatGPT) across 8 reasoning datasets from 3 diverse reasoning genres, including arithmetic, commonsense, and symbolic. Moreover, our analysis proves DBS’s capability of detecting diverse and subtle reasoning errors and robustness on different model scales.
title: Deductive Beam Search: Decoding Deducible Rationale for Chain-of-Thought Reasoning
authors: Tinghui Zhu Kai Zhang Jian Xie Yu Su