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% |
When to Use Thought-Based Reasoning
Use CoT techniques when:
- Multi-step arithmetic or math word problems
- Commonsense reasoning requiring logical deduction
- Symbolic reasoning tasks
- Complex problems where simple prompting fails
Start with:
- Zero-shot CoT for quick prototyping ("Let's think step by step")
- Few-shot CoT when you have good examples
- Self-Consistency for high-stakes decisions
Progressive Loading
L2 Content (loaded when core techniques needed):
- See: references/core-techniques.md
- Chain-of-Thought (CoT) Prompting
- Zero-shot Chain-of-Thought
- Self-Consistency Decoding
- Tree of Thoughts (ToT)
- Least-to-Most Prompting
- ReAct (Reasoning + Acting)
- PAL (Program-Aided Language Models)
- Reflexion
L3 Content (loaded when decision guidance and best practices needed):
- See: references/guidance.md
- Decision Matrix: Which Technique to Use
- Best Practices
- Common Mistakes
- References