  Math Word Problem (MWP) solving presents a challenging task in Natural
Language Processing (NLP). This study aims to provide MWP solvers with a more
diverse training set, ultimately improving their ability to solve various math
problems. We propose several methods for data augmentation by modifying the
problem texts and equations, such as synonym replacement, rule-based: question
replacement, and rule based: reversing question methodologies over two English
MWP datasets. This study extends by introducing a new in-context learning
augmentation method, employing the Llama-7b language model. This approach
involves instruction-based prompting for rephrasing the math problem texts.
Performance evaluations are conducted on 9 baseline models, revealing that
augmentation methods outperform baseline models. Moreover, concatenating
examples generated by various augmentation methods further improves
performance.
