Democratizing Reasoning Ability: Tailored Learning from Large Language
  Model
Zhaoyang Wang Shaohan Huang Yuxuan Liu Jiahai Wang Minghui Song Zihan Zhang Haizhen Huang Furu Wei Weiwei Deng Feng Sun Qi Zhang
Large language models (LLMs) exhibit impressive emergent abilities in natural
language processing, but their democratization is hindered due to huge
computation requirements and closed-source nature. Recent research on advancing
open-source smaller LMs by distilling knowledge from black-box LLMs has
obtained promising results in the instruction-following ability. However, the
reasoning ability which is more challenging to foster, is relatively rarely
explored. In this paper, we propose a tailored learning approach to distill
such reasoning ability to smaller LMs to facilitate the democratization of the
exclusive reasoning ability. In contrast to merely employing LLM as a data
annotator, we exploit the potential of LLM as a reasoning teacher by building
an interactive multi-round learning paradigm. This paradigm enables the student
to expose its deficiencies to the black-box teacher who then can provide
customized training data in return. Further, to exploit the reasoning potential
of the smaller LM, we propose self-reflection learning to motivate the student
to learn from self-made mistakes. The learning from self-reflection and LLM are
all tailored to the student's learning status, thanks to the seamless
integration with the multi-round learning paradigm. Comprehensive experiments
and analysis on mathematical and commonsense reasoning tasks demonstrate the
effectiveness of our method. The code will be available at
https://github.com/Raibows/Learn-to-Reason.