# Math Word Problem Solving by Generating Linguistic Variants of Problem Statements arXiv: https://arxiv.org/abs/2306.13899 ยท HTML: https://mlsanity.com/paper/2306.13899/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Placed in Mathematical reasoning, one of 17 directions in LLM reasoning. - Builds on 49 of its 111 references that are in the map. - First posted on arXiv 24 Jun 2023. ## Placed in - LLM reasoning / [Mathematical reasoning](https://mlsanity.com/lr/mathematical-reasoning/) ## Builds on (most cited first, 30 of 49) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016) - [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015) - [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022) - [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014) - [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023) - [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020) - [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://mlsanity.com/paper/2211.12588/) (2022) - [Are NLP Models really able to Solve Simple Math Word Problems?](https://mlsanity.com/paper/2103.07191/) (2021) - [Deduplicating Training Data Makes Language Models Better](https://mlsanity.com/paper/2107.06499/) (2021) - [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022) - [Graph Transformer Networks](https://mlsanity.com/paper/1911.06455/) (2019) - [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023) - [Solving General Arithmetic Word Problems](https://mlsanity.com/paper/1608.01413/) (2016) - [Large Language Models Are Reasoning Teachers](https://mlsanity.com/paper/2212.10071/) (2022) - [A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers](https://mlsanity.com/paper/2106.15772/) (2021) - [Generating Datasets with Pretrained Language Models](https://mlsanity.com/paper/2104.07540/) (2021) - [Graph Transformer for Graph-to-Sequence Learning](https://mlsanity.com/paper/1911.07470/) (2019) - [Generate & Rank: A Multi-task Framework for Math Word Problems](https://mlsanity.com/paper/2109.03034/) (2021) - [Translating a Math Word Problem to an Expression Tree](https://mlsanity.com/paper/1811.05632/) (2018) - [Neural Paraphrase Generation with Stacked Residual LSTM Networks](https://mlsanity.com/paper/1610.03098/) (2016) - [Reasoning Like Program Executors](https://mlsanity.com/paper/2201.11473/) (2022) - [Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction](https://mlsanity.com/paper/2203.10316/) (2022) ## Built on by (most cited first, 7 of 7) - [Large Language Models for Mathematical Reasoning: Progresses and Challenges](https://mlsanity.com/paper/2402.00157/) (2024) - [What Makes Math Word Problems Challenging for LLMs?](https://mlsanity.com/paper/2403.11369/) (2024) - [Leveraging Large Language Models for Bengali Math Word Problem Solving with Chain of Thought Reasoning](https://mlsanity.com/paper/2505.21354/) (2025) - [Empowering Bengali Education with AI: Solving Bengali Math Word Problems through Transformer Models](https://mlsanity.com/paper/2501.02599/) (2025) - [From Large to Tiny: Distilling and Refining Mathematical Expertise for Math Word Problems with Weakly Supervision](https://mlsanity.com/paper/2403.14390/) (2024) - [Data Augmentation with In-Context Learning and Comparative Evaluation in Math Word Problem Solving](https://mlsanity.com/paper/2404.03938/) (2024) - [A Diversity-Enhanced Knowledge Distillation Model for Practical Math Word Problem Solving](https://mlsanity.com/paper/2501.03670/) (2025) ## Abstract (arXiv) The art of mathematical reasoning stands as a fundamental pillar of intellectual progress and is a central catalyst in cultivating human ingenuity. Researchers have recently published a plethora of works centered around the task of solving Math Word Problems (MWP) - a crucial stride towards general AI. These existing models are susceptible to dependency on shallow heuristics and spurious correlations to derive the solution expressions. In order to ameliorate this issue, in this paper, we propose a framework for MWP solvers based on the generation of linguistic variants of the problem text. The approach involves solving each of the variant problems and electing the predicted expression with the majority of the votes. We use DeBERTa (Decoding-enhanced BERT with disentangled attention) as the encoder to leverage its rich textual representations and enhanced mask decoder to construct the solution expressions. Furthermore, we introduce a challenging dataset, mathrmPsmallARAnormalsizeMAWPS, consisting of paraphrased, adversarial, and inverse variants of selectively sampled MWPs from the benchmark mathrmMsmallAWPS dataset. We extensively experiment on this dataset along with other benchmark datasets using some baseline MWP solver models. We show that training on linguistic variants of problem statements and voting on candidate predictions improve the mathematical reasoning and robustness of the model. We make our code and data publicly available.