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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## Core Techniques

### 1. Chain-of-Thought (CoT) Prompting

**Paper**: "Chain of Thought Prompting Elicits Reasoning in Large Language Models" (Wei et al., 2022)
**Citations**: 14,255+

#### When to Use
- Multi-step arithmetic or math word problems
- Commonsense reasoning requiring logical deduction
- Symbolic reasoning tasks
- When you have good exemplars showing reasoning

#### How It Works
Provide few-shot examples that include intermediate reasoning steps, not just question-answer pairs. The model learns to generate similar step-by-step reasoning.

#### Prompt Template

```
Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?
A: Roger started with 5 balls. 2 cans of 3 tennis balls each is 6 tennis balls. 5 + 6 = 11. The answer is 11.

Q: The cafeteria had 23 apples. If they