Chain-of-Thought Reasoning: Teaching LLMs to Think Step by Step

Large language models can solve complex problems when guided to reason explicitly. Our research advances the science of machine reasoning.

The Reasoning Gap

LLMs often fail on problems requiring:

• Multi-step deduction
• Mathematical manipulation
• Causal reasoning
• Counterfactual thinking

Chain-of-Thought Prompting

By including reasoning steps in prompts, we unlock latent capabilities:

• Show worked examples
• Request explicit reasoning
• Verify intermediate steps
• Encourage self-correction

Verification Methods

We developed techniques to check reasoning:

1. Self-consistency - Sample multiple paths
2. Process reward models - Score each step
3. Formal verification - Check logical validity
4. Execution - Run code snippets

Results

Applications

This enables:

• Automated theorem proving
• Scientific hypothesis generation
• Complex planning tasks
• Educational tutoring systems