Chain-of-Thought (CoT) prompting enhances the reasoning abilities of Large Language Models (LLMs) , enabling them to solve complex reasoning tasks step by step. Challenge: These advanced reasoning capabilities only appear in models with billions of parameters , creating barriers for users limited to Small Language Models (SLMs) . Proposed solution: This paper introduces an Instruction-tuning-CoT method built around CoT demonstrations: Transfers reasoning abilities from large LLMs to smaller SLMs Enables multi-step, controlled reasoning answers via CoT Leverages outputs from robust models to guide smaller ones Results: On question-answering and mathematical reasoning benchmarks, SLMs trained with this method outperform baselines in both in-domain and out-of-domain settings. Reference: ACL Anthology Paper
