Self-Consistency Improves Chain of Thought Reasoning in Language Models

We explore a simple ensemble strategy, self-consistency, that significantly improves the reasoning accuracy of large language models. The idea is to sample a diverse set of reasoning paths from a language model via chain of thought prompting then return the most consistent final answer in the set. We evaluate self-consistency on a range of arithmetic and commonsense reasoning benchmarks, and find that it robustly improves accuracy across a variety of language models and model scales without the need for additional training or auxiliary models. When combined with a recent large language model, PaLM-540B, self-consistency increases performance to state-of-the-art levels across several benchmark reasoning tasks, including GSM8K (56.5% -> 74.4%), SVAMP (79.0% -> 86.6%), AQuA (35.8% -> 48.3%), StrategyQA (75.3% -> 81.6%) and ARC-challenge (85.2% -> 88.7%).