Large Language Models (LLMs) employing Chain-of-Thought (CoT) prompting have
broadened the scope for improving multi-step reasoning capabilities. Usually,
answer calibration strategies such as step-level or path-level calibration play
a vital role in multi-step reasoning. While effective, there remains a
significant gap in our understanding of the key factors that drive their
success. In this paper, we break down the design of recent answer calibration
strategies and present a unified view which establishes connections between
them. We then conduct a thorough evaluation on these strategies from a unified
view, systematically scrutinizing step-level and path-level answer calibration
across multiple paths. Our study holds the potential to illuminate key insights
for optimizing multi-step reasoning with answer calibration.

利用链式思考提问的大型语言模型（LLMs）扩大了改进多步推理能力的范围。本文研究了最近的答案校准策略的设计，并从统一的视角对其进行了全面评估，系统地审查了多个路径上的分步和路径级答案校准。我们的研究有潜力揭示优化多步推理的关键见解。