Chain-of-thought (CoT) via prompting is NOT ALWAYS needed for eliciting reasoning capabilities from large language models (LLMs).
CoT excels in math and logic but underperforms in broader language tasks. So Selective use of CoT can optimize performance without incurring high inference costs.
📚 https://arxiv.org/pdf/2409.12183
Original Problem 🤔:
Chain-of-Thought (CoT) prompting is widely used to enhance reasoning in LLMs. However, its effectiveness across different task types is unclear.
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Solution in this Paper 🛠️:
- Conducted a meta-analysis of over 100 papers and evaluated 20 datasets across 14 models.
- Focused on separating planning and execution stages in problem-solving.
- Compared CoT performance against tool-augmented LLMs.
- Suggested selective application of CoT to reduce inference costs.
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Key Insights from this Paper 💡:
- CoT significantly improves tasks involving math and logic.
- Symbolic reasoning benefits most from CoT, especially in execution.
- Tool augmentation outperforms CoT in symbolic tasks.
- CoT's utility is limited for non-symbolic reasoning tasks.
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Results 📊:
- Math and symbolic reasoning tasks showed substantial improvements with CoT.
- Non-symbolic tasks saw minimal gains.
- On MMLU, CoT's benefit was mostly for questions involving symbolic operations like equations.
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