Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning

TL;DR

This paper explores methods to leverage training data in few-shot prompting for math word problem solving, introducing dynamic program prompting and program distillation to improve model performance and generalization.

Contribution

It proposes two novel approaches, dynamic program prompting and program distillation, to effectively utilize training data in few-shot prompting for numerical reasoning tasks.

Findings

Significant performance improvements over baselines on three MWP datasets.

Enhanced generalization of prompts through training data leveraging.

Boosted performance of small models via program distillation.

Abstract

Chain-of-thought (CoT) prompting with large language models has proven effective in numerous natural language processing tasks, but designing prompts that generalize well to diverse problem types can be challenging, especially in the context of math word problem (MWP) solving. Additionally, it is common to have a large amount of training data that have a better diversity coverage but CoT annotations are not available, which limits the use of supervised learning techniques. To address these issues, we investigate two approaches to leverage the training data in a few-shot prompting scenario: dynamic program prompting and program distillation. Our approach is largely inspired by Gao et al., (2022), where they proposed to replace the CoT with the programs as the intermediate reasoning step. Such a prompting strategy allows us to accurately verify the answer correctness through program execution in MWP solving. Our dynamic program prompting involves annotating the training data by sampling correct programs from a large language model, while program distillation involves adapting a smaller model to the program-annotated training data. Our experiments on three standard MWP datasets demonstrate the effectiveness of these approaches, yielding significant improvements over previous baselines for prompting and fine-tuning. Our results suggest that leveraging a large amount of training data can improve the generalization ability of prompts and boost the performance of fine-tuned small models in MWP solving.