Chain-of-Thought (CoT) is a reasoning technique used in large language models (LLMs) to improve their ability to handle complex tasks. Instead of providing a direct answer, the model generates intermediate steps or explanations that lead to the final output. This process mimics human problem-solving by breaking down tasks into smaller, manageable components.
How It Works:
- Prompting the Model: The model is guided to “think out loud” by including examples of step-by-step reasoning in its training or input prompts.
- Step-by-Step Reasoning: The model generates logical steps or intermediate thoughts before arriving at the final result.
- Improved Understanding: By making its reasoning explicit, the model avoids shortcuts and better handles tasks requiring multi-step calculations or reasoning.
Why It Matters:
Chain-of-Thought enhances performance on tasks like mathematical problem-solving, logic-based questions, and multi-hop reasoning in natural language understanding. It not only improves accuracy but also offers greater transparency, allowing users to understand how the AI arrives at its conclusions.
This approach is especially useful in domains like education, where explaining solutions is as important as providing correct answers, and in applications that require high reliability and interpretability.
