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Self-Consistency Improves Chain of Thought Reasoning in Language Models

ICLR 2023 · Large language models › Reasoning and chain of thought · approach: Long CoT dynamics and efficiency · 706 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace greedy CoT decoding with 40 sampled reasoning paths and answer majority votingPaLM-540B · GSM8Kaccuracy: 56.5 -> 74.4 (+17.9)Helped
Replace greedy CoT decoding with sampled paths and answer aggregationPaLM-540B · AQuAaccuracy: 35.8 -> 48.3 (+12.5)Helped
Replace greedy CoT decoding with sampled paths and answer aggregationPaLM-540B · StrategyQAaccuracy: 75.3 -> 81.6 (+6.3)Helped
Replace greedy CoT decoding with sampled paths and answer aggregationPaLM-540B · ARC-challengeaccuracy: 85.2 -> 88.7 (+3.5)Helped
Use unweighted majority voting instead of normalized weighted answer summation (ablation)Self-consistency · GSM8Kaccuracy: 74.1 -> 74.4No clear effect
Use unnormalized rather than normalized sequence probabilities when weighting answers (ablation)Self-consistency · GSM8Kaccuracy: 74.1 -> 59.9Hurt
Use weighted average rather than weighted sum for answer aggregation (ablation)Self-consistency · GSM8Kaccuracy: 74.1 -> 22.1Hurt
Use beam search instead of sampling to generate paths (ablation)Self-consistency on UL2-20B · AQuAaccuracy: 26.9 -> 24.2Hurt
Use beam search instead of sampling to generate paths (ablation)Self-consistency on UL2-20B · MultiArithaccuracy: 14.7 -> 10.8Hurt
Add self-consistency over 40 paths to an imperfect CoT prompt (ablation)LaMDA-137B · GSM8Kaccuracy: 14.9 -> 23.4Helped
Add self-consistency to equation-based CoT prompting (ablation)LaMDA-137B · GSM8Kaccuracy: 5.0 -> 6.5Helped
Add self-consistency to zero-shot CoT (ablation)PaLM-540B · GSM8Kaccuracy: 43.0 -> 69.2Helped

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