thought-based-reasoninglisted

# 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% | --- ## Core Techniques ### 1. Chain-of-Thought (CoT) Prompting **Paper**: "Chain of Thought Prompting Elicits Reasoning in Large Language Models" (Wei et al., 2022) #### 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 Wor