因果充分性与必要性提升思维链推理
Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning
AI总结：
本文提出因果充分性与必要性框架，用于优化思维链推理，自动添加缺失步骤并剪除冗余步骤，在数学和常识基准上提升效率并减少令牌使用，且不损失准确性。
摘要：
思维链（Chain-of-Thought, CoT）提示在赋予大型语言模型（LLMs）复杂推理能力方面发挥着不可或缺的作用。然而，CoT目前面临两个基本挑战：（1）充分性，即确保生成的中间推理步骤全面覆盖并证实最终结论；（2）必要性，即识别对于最终答案的正确性真正不可或缺的推理步骤。我们提出了一个因果框架，通过充分性和必要性这两个视角来刻画CoT推理。结合因果充分性概率和必要性概率，我们不仅能够确定哪些步骤在逻辑上对预测结果充分或必要，还能量化它们在不同干预场景下对最终推理结果的实际影响，从而能够自动添加缺失步骤并剪除冗余步骤。在多种数学和常识推理基准上的广泛实验结果证实，在不牺牲准确性的前提下，推理效率显著提升，令牌使用量减少。我们的工作为提高LLM推理性能和成本效益提供了一个有前景的方向。
英文摘要：
Chain-of-Thought (CoT) prompting plays an indispensable role in endowing large language models (LLMs) with complex reasoning capabilities. However, CoT currently faces two fundamental challenges: (1) Sufficiency, which ensures that the generated intermediate inference steps comprehensively cover and substantiate the final conclusion; and (2) Necessity, which identifies the inference steps that are truly indispensable for the soundness of the resulting answer. We propose a causal framework that characterizes CoT reasoning through the dual lenses of sufficiency and necessity. Incorporating causal Probability of Sufficiency and Necessity allows us not only to determine which steps are logically sufficient or necessary to the prediction outcome, but also to quantify their actual influence on the final reasoning outcome under different intervention scenarios, thereby enabling the automated addition of missing steps and the pruning of redundant ones. Extensive experimental results on various mathematical and commonsense reasoning benchmarks confirm substantial improvements in reasoning efficiency and reduced token usage without sacrificing accuracy. Our work provides a promising direction for improving LLM reasoning performance and cost-effectiveness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
