Advancing AI Reasoning: Meta-CoT and System 2 Thinking | by Kaushik Rajan | Jan, 2025

How Meta-CoT enhances system 2 reasoning for advanced AI challenges

What makes a language mannequin sensible? Is it predicting the following phrase in a sentence ‒ or dealing with robust reasoning duties that problem even vibrant people? Right now’s Giant Language Fashions (LLMs) create clean textual content plus remedy easy issues however they battle with challenges needing cautious thought, like onerous math or summary problem-solving.

This difficulty comes from how LLMs deal with info. Most fashions use System 1-like considering ‒ quick, sample primarily based reactions much like instinct. Whereas it really works for a lot of duties, it fails when issues want logical reasoning together with attempting totally different approaches and checking outcomes. Enter System 2 considering ‒ a human methodology for tackling onerous challenges: cautious, step-by-step ‒ usually needing backtracking to enhance conclusions.

To repair this hole, researchers launched Meta Chain-of-Thought (Meta-CoT). Constructing on the favored Chain-of-Thought (CoT) methodology, Meta-CoT lets LLMs mannequin not simply steps of reasoning however the entire means of “considering by way of an issue.” This alteration is like how people sort out robust questions by exploring together with evaluating ‒ and iterating towards solutions.