Title: Self-Consistency Improves Chain of Thought Reasoning in Language Models
Authors: Xuezhi Wang, Jason Wei, Dale Schuurmans et al.
Year: 2022
arXiv: 2203.11171 (https://arxiv.org/abs/2203.11171)

Abstract:
Chain-of-thought prompting combined with pre-trained large language models has achieved
encouraging results on complex reasoning tasks. In this paper, we propose a new decoding
strategy, self-consistency, to replace the naive greedy decoding used in chain-of-thought
prompting. It first samples a diverse set of reasoning paths instead of only taking the
greedy one, and then selects the most consistent answer by marginalizing out the sampled
reasoning paths. Self-consistency leverages the intuition that a complex reasoning problem
typically admits multiple different ways of thinking leading to its unique correct answer.
Our extensive empirical evaluation shows that self-consistency boosts the performance of
chain-of-thought prompting with a striking margin on a range of popular arithmetic and
commonsense reasoning benchmarks, including GSM8K (+17.9%), SVAMP (+11.0%), AQuA (+12.2%),
StrategyQA (+6.4%) and ARC-challenge (+3.9%).
