Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step
  Reasoning
Yiwei Li Peiwen Yuan Shaoxiong Feng Boyuan Pan Xinglin Wang Bin Sun Heda Wang Kan Li
Self-consistency (SC) has been a widely used decoding strategy for
chain-of-thought reasoning. Despite bringing significant performance
improvements across a variety of multi-step reasoning tasks, it is a high-cost
method that requires multiple sampling with the preset size. In this paper, we
propose a simple and scalable sampling process, \textbf{E}arly-Stopping
\textbf{S}elf-\textbf{C}onsistency (ESC), to greatly reduce the cost of SC
without sacrificing performance. On this basis, one control scheme for ESC is
further derivated to dynamically choose the performance-cost balance for
different tasks and models. To demonstrate ESC's effectiveness, we conducted
extensive experiments on three popular categories of reasoning tasks:
arithmetic, commonsense and symbolic reasoning over language models with
varying scales. The empirical results show that ESC reduces the average number
of sampling of chain-of-thought reasoning by a significant margin on six
benchmarks, including MATH (-33.8%), GSM8K (-80.1%), StrategyQA (-76.8%),
CommonsenseQA (-78.5%), Coin Flip (-84.2%) and Last Letters (-67.4%), while
attaining comparable performances.