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 fail to address
reasoning errors in intermediate steps, leading 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.

通过 Deductive Beam Search 的有机结合，我们的方法显著提高了不同规模的 Large Language Models 在算术、常识和符号等 3 个推理领域的性能，并证明其在检测多样且微妙推理错误以及对不同模型规模的鲁棒性方面的能力。