From Large to Tiny: Distilling and Refining Mathematical Expertise for Math Word Problems with Weakly Supervision

• URL: http://arxiv.org/abs/2403.14390v1
• Date: Thu, 21 Mar 2024 13:29:54 GMT
• Title: From Large to Tiny: Distilling and Refining Mathematical Expertise for Math Word Problems with Weakly Supervision
• Authors: Qingwen Lin, Boyan Xu, Zhengting Huang, Ruichu Cai,
• Abstract summary: We introduce an innovative two-stage framework that adeptly transfers mathematical Expertise from large to tiny language models. Our method fully leverages the semantic understanding capabilities during the searching 'problem-equation' pair. It demonstrates significantly improved performance on the Math23K and Weak12K datasets compared to existing small model methods.
• Score: 12.023661884821554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Addressing the challenge of high annotation costs in solving Math Word Problems (MWPs) through full supervision with intermediate equations, recent works have proposed weakly supervised task settings that rely solely on the final answer as a supervised signal. Existing leading approaches typically employ various search techniques to infer intermediate equations, but cannot ensure their semantic consistency with natural language descriptions. The rise of Large Language Models (LLMs) like ChatGPT has opened up new possibilities for addressing MWPs directly. However, the computational demands of LLMs make them less than ideal for use in settings where resources are tight. In light of these challenges, we introduce an innovative two-stage framework that adeptly transfers mathematical Expertise from large to tiny language models. In \emph{Distillation Stage}, we propose a series of extraction processes that satisfy the properties of MWPs to distill mathematical knowledge from LLMs to construct problem-equation pairs required for supervised training. In \emph{Refinement Stage}, Due to Knowledge distilling method cannot guarantee the full utilization of all data, we further utilize the unsuccessfully searched data effectively by Knowledge Refine method. Finally, We train a small model using distilled data generated through two-stage methods. As our method fully leverages the semantic understanding capabilities during the searching 'problem-equation' pair, it demonstrates significantly improved performance on the Math23K and Weak12K datasets compared to existing small model methods, while maintaining a much lower computational cost than ChatGPT.

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