thought-based-reasoning
Thought-Based Reasoning Techniques for LLMs
Overview
Chain-of-Thought (CoT) prompting and its variants encourage LLMs to generate intermediate reasoning steps before arriving at a final answer, significantly improving performance on complex reasoning tasks. These techniques transform how models approach problems by making implicit reasoning explicit.
Quick Reference
| Technique | When to Use | Complexity | Accuracy Gain |
|---|---|---|---|
| Zero-shot CoT | Quick reasoning, no examples available | Low | +20-60% |
| Few-shot CoT | Have good examples, consistent format needed | Medium | +30-70% |
| Self-Consistency | High-stakes decisions, need confidence | Medium | +10-20% over CoT |
| Tree of Thoughts | Complex problems requiring exploration | High | +50-70% on hard tasks |
| Least-to-Most | Multi-step problems with subproblems | Medium | +30-80% |
| ReAct | Tasks requiring external information | Medium | +15-35% |
| PAL | Mathematical/computational problems | Medium | +10-15% |
| Reflexion | Iterative improvement, learning from errors | High | +10-20% |
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