Logic Consistency Makes Large Language Models Personalized Reasoning Teachers

Abstract

Large Language Models (LLMs) have advanced natural language processing, particularly through Chain-of-Thought (CoT) reasoning, but their high computational costs limit deployment. We propose Personalized Chain-of-Thought Distillation (PeCoTD), a method that transfers CoT reasoning from LLMs to smaller models by addressing the distribution gap—the difference in how large and small models process information. To bridge this gap, PeCoTD introduces the Self Logic Consistency (SLC) metric, which helps small models evaluate and select LLM-generated rationales that align better with their reasoning abilities. PeCoTD iteratively refines these rationales, adjusting them to better fit the learning patterns of small models while preserving their original meaning. Experiments show PeCoTD significantly enhances the reasoning abilities of small models across datasets, making CoT distillation more practical and effective.

Bingyuan Zhang

University of Science and Technology of China

Xulong Zhang

Executive Director

[email protected]

Jianzong Wang

Honorary Director

[email protected]