  Large Language Models (LLMs) employing Chain-of-Thought (CoT) prompting have
broadened the scope for improving multi-step reasoning capabilities. We
generally divide multi-step reasoning into two phases: path generation to
generate the reasoning path(s); and answer calibration post-processing the
reasoning path(s) to obtain a final answer. However, the existing literature
lacks systematic analysis on different answer calibration approaches. In this
paper, we summarize the taxonomy of recent answer calibration techniques and
break them down into step-level and path-level strategies. 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. Experimental results reveal that integrating the dominance of both
strategies tends to derive optimal outcomes. Our study holds the potential to
illuminate key insights for optimizing multi-step reasoning with answer
calibration.
