Improving AI Thinking: Meta-CoT and System 2 Thinking | by Kaushik Rajan | January, 2025

How Meta-CoT improves systems thinking 2 for complex AI challenges

What makes a language model intelligent? Is it predicting the next word in a sentence ‒ or managing difficult thinking tasks that challenge even bright people? Today's Large-Scale Language Models (LLMs) create fluent text and solve simple problems but struggle with challenges that require careful consideration, such as complex math or solving abstract problems.

This issue arises from the way LLMs handle information. Many models use System 1-like thinking ‒ fast, pattern-based responses such as search. Although it works in many tasks, it fails when problems require logical thinking and trying different methods and evaluating the results. Enter System 2 thinking ‒ the human way of dealing with difficult challenges: carefully, step by step ‒ often requiring backtracking to improve results.

To address this gap, researchers introduced the Meta Chain-of-Thought (Meta-CoT). Building on the popular Chain-of-Thought (CoT) method, Meta-CoT allows LLMs to demonstrate not just the steps of thinking but the entire process of “thinking about a problem.” This change is similar to how people approach difficult questions by testing and checking ‒ and repeating answers.