Meta-Reasoning Framework for Enhancing Multi-Step Inference in Large Language Models

Abstract

Large Language Models (LLMs) have shown impressive capabilities in natural language processing activities, but do not yet perform effective multi-step reasoning and commonly generate logically inconsistent intermediary steps. This is because it is limited by the failure to control reasoning in a structured manner, and insufficient measures to justify intermediate claims. In a bid to mitigate this challenge, a Meta-Reasoning Learning Framework is proposed which explicitly represents reasoning as a multi stage process that incorporates planning, step-wise inference and self-reflective validation. The framework proposes a pipeline that is coordinated to bring out a model that would generate a reasoning blue print, guided inference and repeatable refinement of a reasoning, using feedback that is used to correct the reasoning. The suggested method is tested on a variety of benchmark reasoning problems, showing great improvements compared to baseline models. The results of the experiments have demonstrated that the framework has an accuracy of 91.3, and it is stronger than the strongest base by about 6 or 8, besides the reasoning consistency of 0.88 and error correction rate of 46.5. These results indicate enhanced robustness and reliability in multi-step inference. The results point out that the use of systematic meta-reasoning and refinement processes significantly enhance performance and reasoning. The work offers a legitimate and explainable method towards progressive credible reasoning in LLMs, and possible applications in complex decision-making and intelligent systems