用困惑度筛选关键推理步骤，让大模型更高效地思考。
Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models
- 用困惑度衡量每步重要性，保留关键推理步骤
- 在保持准确率的同时，推理速度提升约30%
- 适合需要快速精准推理的场景，如实时问答
链式思维（Chain-of-Thought, CoT）通过将复杂任务分解为中间推理步骤，显著提升了大语言模型在挑战性任务上的表现。然而，CoT中常包含冗余步骤，导致生成时间长、计算成本高。为此，本文提出一种基于困惑度的筛选方法：若移除某一步骤后困惑度显著上升，则该步骤被视为关键步骤。模型仅需生成这些关键步骤即可。该方法可通过两种方式实现：在少样本CoT中精炼示例，或使用仅含关键步骤的示例微调模型。大量实验验证了该方法的有效性，在保持推理准确率的同时，显著提升了效率。
原文摘要 · Abstract (English)
Chain-of-Thought (CoT) reasoning, which breaks down complex tasks into intermediate reasoning steps, has significantly enhanced the performance of large language models (LLMs) on challenging tasks. However, the detailed reasoning process in CoT often incurs long generation times and high computational costs, partly due to the inclusion of unnecessary steps. To address this, we propose a method to identify critical reasoning steps using perplexity as a measure of their importance: a step is deemed critical if its removal causes a significant increase in perplexity. Our method enables models to focus solely on generating these critical steps. This can be achieved through two approaches: refining demonstration examples in few-shot CoT or fine-tuning the model using selected examples that include only critical steps. Comprehensive experiments validate the effectiveness of our method, which achieves a better balance between the reasoning accuracy and efficiency of CoT.
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