Deductive Beam Search: Decoding Deducible Rationale for Chain-of-Thought Reasoning
CoRR(2024)
摘要
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.
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