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

arXiv 2404.03938 ↗First posted 5 Apr 2024cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 67 references that are in the map. First posted on arXiv 5 Apr 2024.

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References · 28

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16 more
  1. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
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  3. 2018Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problemscited by 49
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  5. 2021MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solverscited by 39
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  9. 2021MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solvingcited by 32
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  16. 2023Math Word Problem Solving by Generating Linguistic Variants of Problem Statementscited by 7

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