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Distilling Algorithmic Reasoning from LLMs via Explaining Solution Programs
April 15, 2024, 4:46 a.m. | Jierui Li, Raymond Mooney
cs.CL updates on arXiv.org arxiv.org
Abstract: Distilling explicit chain-of-thought reasoning paths has emerged as an effective method for improving the reasoning abilities of large language models (LLMs) across various tasks. However, when tackling complex tasks that pose significant challenges for state-of-the-art models, this technique often struggles to produce effective chains of thought that lead to correct answers. In this work, we propose a novel approach to distill reasoning abilities from LLMs by leveraging their capacity to explain solutions. We apply our …
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