Towards A Unified View of Answer Calibration for Multi-Step Reasoning

arXiv:2311.09101v2 Announce Type: replace-cross
Abstract: 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 …

abstract arxiv capabilities cs.ai cs.cl cs.ir cs.lg generate language language models large language large language models llms path post-processing processing prompting reasoning thought type view