Chain-of-Thought (CoT) prompting can enhance the reasoning capabilities of
large language models (LLMs), establishing itself as a primary approach to
solving complex reasoning tasks. Existing CoT synthesis approaches usually
focus on simpler reasoning tasks and thus result in low-quality and
inconsistent CoT prompts. In response to this challenge, we present an
empirical investigation of CoT prompting and introduce CoTGenius, a novel
framework designed for the automatic generation of superior CoT prompts.
CoTGenius is developed based on three major evolution strategies, i.e.,
complicate, diversify, and specify-alongside two filtering mechanisms:
evolutionary success judgement and correctness verification. We further employ
CoTGenius to create an extensive CoT dataset, and subsequently fine-tune the
Llama 2-Chat 7B and 13B models on this dataset. We call the resulting model
ChainLM. To deal with the cumulative error issue in reasoning steps, we propose
a step-level debating method, wherein multiple debaters discuss each reasoning
step to arrive at the correct answer. Extensive experiments demonstrate that
our ChainLM models exhibit enhanced proficiency in addressing a spectrum of
complex reasoning problems compared to existing models. In addition, we conduct
an in-depth analysis of the impact of data categories within CoTGenius on the
model performance. We release our dataset and code at
this https URL

Chain-of-Thought 提示可以增强大型语言模型（LLMs）的推理能力，现有的 CoT 综合方法针对简单推理任务，导致 CoT 提示的质量低且不一致。为了应对这一挑战，我们进行了 CoT 提示的实证研究并引入了 CoTGenius，一个用于自动生成优质 CoT 提示的新框架。我们进一步采用 CoTGenius 创建了一个广泛的 CoT 数据集，并在该数据集上对 Llama 2-Chat 7B 和 13B 模型进行了微调，得到了 ChainLM 模型。为了解决推理步骤中的累积误差问题，我们提出了一种步骤级辩论方法，其中多个辩论者讨论每个推理步骤，得出正确答案。大量实验证明，与现有模型相比，我们的 ChainLM 模型在解决一系列复杂推理问题方面表现出更高的熟练度。此外，我们对 CoTGenius 中的数据类别对模型性能的影响进行了深入分析。我们在此 https URL 上发布了我们的数据集和代码。