“Math word problem generation with mathematical consistency and problem context constraints,”
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We study the problem of generating arithmetic math word problems (MWPs) given a math equation that specifies the mathematical computation and a context that specifies the problem scenario.Existing approaches are prone to generating MWPs that are either mathematically invalid or have unsatisfactory language quality.They also either ignore the context or require manual specification of a problem template, which compromises the diversity of the generated MWPs.In this paper, we develop a novel MWP generation approach that leverages i) pre-trained language models and a context keyword selection model to improve the language quality of the generated MWPs and ii) an equation consistency constraint for math equations to improve the mathematical validity of the generated MWPs.Extensive quantitative and qualitative experiments on three realworld MWP datasets demonstrate the superior performance of our approach compared to various baselines.
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More advanced techniques aim at watermarking the content generated by language models.
“More advanced techniques aim at watermarking the content generated by language models [64, 65], e.g., by biasing the content generation towards terms, which are rather unlikely to be jointly used by humans in a text passage.”
From ChatGPT for good? On opportunities and challenges of large language models for education (2023) · cites this paper as [65]
The Methodology and Conclusions sections describe generating math word problems with equation and context constraints, not watermarking language-model output. Searches for watermarking and for biasing generation toward unusual combinations of terms found no passage addressing the attributed claim. (LLM, conf=0.77)
Experimental — accuracy not yet measuredSubsequent research on automatic MWP generation has focused on improving topic relevance, expression relevance, language coherence, as well as completeness and validity of the generated problems.
“Subsequent research along this direction has focused on improving topic relevance, expression relevance, language coherence, as well as completeness and validity of the generated problems using a spectrum of approaches (Liu et al., 2021; Wang et al., 2021; Wu et al., 2022).”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Wang et al., 2021
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Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank
Kumar, N. A., Fernandez, N., Wang, Z., & Lan, A. (2023) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/2023.bea-1.22 · Cited by 7 · see citing works
Rathi, S. R., Pophale, V., Kutwal, P., Mishra, S., & Nadkarni, A. (2024) · IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE) · doi:10.1109/icwite59797.2024.10503319 · Cited by 6 · see citing works
Smolansky, A., Yang, M., & Azenkot, S. (2024) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3663548.3675636 · Cited by 6 · see citing works
The Efficiency of Teacher-Driven Context Personalization in Mathematics with Large Language Models
Walkington, C., Beauchamp, T., Lan, A., & Pruitt-Britton, T. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98459-4_7 · Cited by 5 · see citing works
Shen, C., Chen, Q., Zhang, N., Diao, F., Liu, P., & Zhou, X. (2024) · European Journal of Psychology of Education · doi:10.1007/s10212-024-00924-4 · Cited by 3 · see citing works
Bidirectional Training for Generating Math Word Problems Using Pre-Trained Model and Prompt
Kai, G. (2023) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip60502.2023.10387053 · Cited by 2 · see citing works
Grand Challenges in AI and Education Beyond 2030
Woolf, B. P., Allessio, D., Arroyo, I., Gattupalli, S., & Zhang, B. (2025) · Interaction design & architecture(s)/ID&A Interaction design & architecture(s) · doi:10.55612/s-5002-064-001sp · Cited by 2 · see citing works
Knowledge Base Question Generation via Data Augmentation with Dynamic-Prompt
Zhao, L., Yin, X., Wang, Y., & Li, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72350-6_17 · Cited by 2 · see citing works
Validity Checking and Repairing of Machine Generated Transfer-Type Word Problems
Kumar, S., & Kumar, P. S. (2024) · Applied Ontology · doi:10.1177/15705838241303829 · Cited by 1 · see citing works
Question fuzzy-attention embedding Graph-to-Tree network for intelligent math word problem solver
Lang, Q., Yin, M., Wang, Z., Liu, X., & Liang, S. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130850 · Cited by 1 · see citing works
An exploration into the nature of ChatGPT’s mathematical knowledge
Marco, N., & Stylianides, A. J. (2025) · International Journal of Mathematical Education in Science and Technology · doi:10.1080/0020739x.2025.2543817 · Cited by 2 · see citing works
The differential developmental trajectory for symbolic and situational mathematics abilitiesPreprint
Shen, C., Chen, Q., Zhang, N., Diao, F., Liu, P., & Zhou, X. (2023) · doi:10.31234/osf.io/tak9y · Cited by 1 · see citing works
Math Word Problem Generation with Memory Retrieval
Wang, X., Qin, W., Hu, Z., Wang, L., Lan, Y., & Hong, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18913-5_29
Towards an Automated Assistant for Generating Mathematics Problems
Mahlaza, Z. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-21076-1_3
"A PIECE OF CAKE" - SUSTAINABLE EDUCATION PRACTICES WITH TRANSFORMERMODELS
de Arriba-Pérez, F., & González-Castaño, S. G.-M. A. F. J. (2023) · IADIS International Conference on e-Learning and Digital Learning and International Conference on Sustainability, Technology and Education (ELDL/STE) · doi:10.33965/el_ste2023_202303l019
A Multilingual Dataset (MultiMWP) and Benchmark for Math Word Problem Generation
Gamage, O., Ranathunga, S., Lee, A., Sun, X. N., Singh, A., Skënduli, M. P., Alam, M., Nayak, A. K., Gao, H., Deori, B., et al. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3552936
Enabling Interactive Education With Low‐Latency Large Language Models
Wu, W., Guo, L., Yu, W., Chen, T., Ruan, X., & Li, S. (2025) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70407 · Cited by 1 · see citing works
Generating and Solving Complex Transfer Type Arithmetic Word Problems
Kumar, S., & Kumar, P. (2026) · Semantic Web · doi:10.1177/22104968261440709
Generating Math Word Problems Aligned with Pupil Ability and Item Difficulty
Wang, J., & Yu, X. (2025) · International Conference on Intelligent Education and Intelligent Research (IEIR) · doi:10.1109/ieir67391.2025.11491032
Методика применения онтологии олимпиадных математических задач с нестандартными элементами
Нуякшин, М. Г., & Черемисина, Е. Н. (2025) · Sovremennye informacionnye tehnologii i IT-obrazovanie. · doi:10.25559/sitito.021.202502.306-316
Utami, I. Q., & Hwang, W. (2026) · International Journal of Science and Mathematics Education · doi:10.1007/s10763-026-10715-6
- Authors
- Wang, Z., Lan, A., & Baraniuk, R.
- Affiliations
- Rice University (US)
- University of Massachusetts Amherst (US)
- Corresponding author
- Lan, Andrew
- Venue
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Published
- 2021
- Pages
- 5986-5999
- OpenAlex
- W3198965934
- Cited by
- 39
- References
- 71
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 11 · 2023: 13 · 2022: 3
- FWCI
- 3.11
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1917713, 1842378, 1937134)
- Office of Naval Research (N00014-18, FA9550, N00014-18-1-2047, N00014)
- Air Force Office of Scientific Research (FA9550-18-1-0478, FA9550-)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Mathematics, Computing, and Information Processing
- Keywords
- arithmetic word problems, mathematical consistency, pre-trained language models, problem context, language quality
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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