What makes a language model smart? Is it predicting the subsequent word in a sentence ‒ or handling tough reasoning tasks that challenge even brilliant humans? Today’s Large Language Models (LLMs) create smooth text plus solve easy problems but they struggle with challenges needing careful thought, like hard math or abstract problem-solving.
This issue comes from how LLMs handle information. Most models use System 1-like considering ‒ fast, pattern based reactions just like intuition. While it really works for a lot of tasks, it fails when problems need logical reasoning together with trying different approaches and checking results. Enter System 2 considering ‒ a human method for tackling hard challenges: careful, step-by-step ‒ often needing backtracking to enhance conclusions.
To repair this gap, researchers introduced Meta Chain-of-Thought (Meta-CoT). Constructing on the favored Chain-of-Thought (CoT) method, Meta-CoT lets LLMs model not only steps of reasoning but the entire technique of “considering through an issue.” This transformation is like how humans tackle tough questions by exploring together with evaluating ‒ and iterating toward answers.
