Research map / Large language models / Reasoning and chain of thought
Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add chain of thought to a 3-layer transformer (ablation) | 3-layer transformer · Arithmetic formula, 6 operators | accuracy (%): 17.72 -> 99.97 | Helped |
| add a layer without CoT (3 to 5 layers) (ablation) | transformer without CoT · Arithmetic formula, 6 operators | accuracy (%): 17.72 -> 46.19 | Helped |
| train on <=14 operators, test on longer formulas | 3-layer transformer with CoT · Arithmetic formula length generalization | accuracy (%): 99.9 -> 45.5 (15 to 18 operators) | Mixed |
Closest papers by meaning
- To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning (ICLR 2025 · similarity 0.92)
- Chain of Thoughtlessness? An Analysis of CoT in Planning (NeurIPS 2024 · similarity 0.92)
- How Does Chain of Thought Think? Mechanistic Interpretability of Chain-of-Thought Reasoning with Sparse Autoencoding (AAAI 2026 · similarity 0.91)
- Mind the Gap: Bridging Thought Leap for Improved Chain-of-Thought Tuning (NeurIPS 2025 · similarity 0.91)
- Analyzing the Power of Chain of Thought through Memorization Capabilities (NeurIPS 2025 · similarity 0.91)
- Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns (AAAI 2025 · similarity 0.91)
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