Research map / Large language models / Reasoning and chain of thought
Self-Consistency Improves Chain of Thought Reasoning in Language Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace greedy CoT decoding with 40 sampled reasoning paths and answer majority voting | PaLM-540B · GSM8K | accuracy: 56.5 -> 74.4 (+17.9) | Helped |
| Replace greedy CoT decoding with sampled paths and answer aggregation | PaLM-540B · AQuA | accuracy: 35.8 -> 48.3 (+12.5) | Helped |
| Replace greedy CoT decoding with sampled paths and answer aggregation | PaLM-540B · StrategyQA | accuracy: 75.3 -> 81.6 (+6.3) | Helped |
| Replace greedy CoT decoding with sampled paths and answer aggregation | PaLM-540B · ARC-challenge | accuracy: 85.2 -> 88.7 (+3.5) | Helped |
| Use unweighted majority voting instead of normalized weighted answer summation (ablation) | Self-consistency · GSM8K | accuracy: 74.1 -> 74.4 | No clear effect |
| Use unnormalized rather than normalized sequence probabilities when weighting answers (ablation) | Self-consistency · GSM8K | accuracy: 74.1 -> 59.9 | Hurt |
| Use weighted average rather than weighted sum for answer aggregation (ablation) | Self-consistency · GSM8K | accuracy: 74.1 -> 22.1 | Hurt |
| Use beam search instead of sampling to generate paths (ablation) | Self-consistency on UL2-20B · AQuA | accuracy: 26.9 -> 24.2 | Hurt |
| Use beam search instead of sampling to generate paths (ablation) | Self-consistency on UL2-20B · MultiArith | accuracy: 14.7 -> 10.8 | Hurt |
| Add self-consistency over 40 paths to an imperfect CoT prompt (ablation) | LaMDA-137B · GSM8K | accuracy: 14.9 -> 23.4 | Helped |
| Add self-consistency to equation-based CoT prompting (ablation) | LaMDA-137B · GSM8K | accuracy: 5.0 -> 6.5 | Helped |
| Add self-consistency to zero-shot CoT (ablation) | PaLM-540B · GSM8K | accuracy: 43.0 -> 69.2 | Helped |
Closest papers by meaning
- Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning (ICLR 2024 · similarity 0.92)
- Optimal Self-Consistency for Efficient Reasoning with Large Language Models (ICML 2026 · similarity 0.91)
- SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures (NeurIPS 2024 · similarity 0.91)
- Chain-of-Thought Reasoning Without Prompting (NeurIPS 2024 · similarity 0.91)
- How Does Chain of Thought Think? Mechanistic Interpretability of Chain-of-Thought Reasoning with Sparse Autoencoding (AAAI 2026 · similarity 0.91)
- Reliable Chain-of-Thought via Prefix Consistency (NeurIPS 2026 · similarity 0.90)
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