Data Augmentation with In-Context Learning and Comparative Evaluation in Math Word Problem Solving

arXiv:2404.03938v1 Announce Type: new
Abstract: 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 …

abstract arxiv augmentation context cs.cl data diverse evaluation improving in-context learning language language processing math natural natural language natural language processing nlp processing set solve study training type word