Papers with MWPs
LogicSolver: Towards Interpretable Math Word Problem Solving with Logical Prompt-enhanced Learning (2022.findings-emnlp)
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| Challenge: | Recent advances in MWP solving are uninterpretable due to shallow heuristics . a new approach to solve automatic word problem solvers requires a solver to predict expression tree and corresponding linguistic logic formulas simultaneously. |
| Approach: | They propose to annotate interpretable logical formulas based on algebraic knowledge as the grounded linguistic logic of each solution equation. |
| Outcome: | The proposed approach improves interpretability of a MWP solver by using logical prompts and interpretation generation. |
Error Classification of Large Language Models on Math Word Problems: A Dynamically Adaptive Framework (2025.findings-emnlp)
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| Challenge: | Current error classification methods rely on static and predefined categories to capture error patterns. |
| Approach: | They propose a framework for automated dynamic error classification in mathematical reasoning that incorporates common error patterns as explicit guidance. |
| Outcome: | The proposed framework reduces human bias and fine-grained analysis of error patterns. |
ArMATH: a Dataset for Solving Arabic Math Word Problems (2022.lrec-1)
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| Challenge: | This paper is the first to use deep learning methods to solve Arabic MWPs . it is also the first study to use transfer learning to solve MWp across different languages . |
| Approach: | They contribute to the first large-scale dataset for Arabic Math Word Problems . they use deep learning methods to solve Arabic MWPs and a transfer learning model to promote performance . |
| Outcome: | The proposed model improves Arabic MWP solvers by 3% over the existing model. |
A Meaning-Based Statistical English Math Word Problem Solver (N18-1)
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| Challenge: | Experimental results show that the proposed approach understands the meaning of each quantity in the text more. |
| Approach: | They propose a meaning-based approach for solving English math word problems . they analyze text, transform body and question parts into corresponding logic forms . Statistical models are proposed to select operator and operands . |
| Outcome: | The proposed approach outperforms existing systems on benchmark and noisy datasets. |
What Makes Math Word Problems Challenging for LLMs? (2024.findings-naacl)
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| Challenge: | Experiments show that even quite powerful LLMs are still challenged by MWPs. |
| Approach: | They propose to analyze what makes math word problems (MWPs) in English challenging for large language models (LLMs). |
| Outcome: | The proposed model can handle a range of core NLP tasks, but it has emergent abilities, such as ability to solve mathematical puzzles. |
Ask-Before-Detection: Identifying and Mitigating Conformity Bias in LLM-Powered Error Detector for Math Word Problem Solutions (2025.acl-long)
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| Challenge: | Recent studies have demonstrated the potential of large language models (LLMs) for automatic error detection in math word problems (MWPs). |
| Approach: | They propose a framework that generates adaptive reference solutions using LLMs to enhance error detection by reducing conformity bias in MWPs. |
| Outcome: | The proposed framework mitigates the performance gap between conventional and alternative solutions in MWPs, especially when combined with reasoning-enhancing techniques like chain-of-thought prompting. |
A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers (2020.acl-main)
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| Challenge: | Existing MWP corpora are limited in language patterns and problem types . a new corpus of 2,305 MWps is proposed that is more diverse in terms of lexicon usage . |
| Approach: | They propose to use ASDiv to measure lexicon usage diversity of a given MWP corpus. |
| Outcome: | The proposed corpus covers more problem types and text patterns than existing corpora and reflects the true capability of solvers more faithfully. |
Unbiased Math Word Problems Benchmark for Mitigating Solving Bias (2022.findings-naacl)
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| Challenge: | Existing solvers with data bias and learning bias only learn shallow heuristics rather than deep semantics for understanding problems. |
| Approach: | They propose a MWP dataset named UnbiasedMWP which is constructed by varying the grounded expressions in collected data and annotating them manually. |
| Outcome: | The proposed dataset has significantly fewer biases than its original data and other datasets, posing a promising benchmark for fairly evaluating the solvers’ reasoning skills rather than matching nearest neighbors. |
An Augmented Benchmark Dataset for Geometric Question Answering through Dual Parallel Text Encoding (2022.coling-1)
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| Challenge: | Existing methods for solving geometric problems are limited due to lack of high-quality datasets and efficient neural solvers. |
| Approach: | They propose to annotate 2,518 geometric problems with richer types and greater difficulty using a benchmark dataset. |
| Outcome: | The proposed method improves the accuracy of automatic geometric problem solving to 66.09%. |
Are NLP Models really able to Solve Simple Math Word Problems? (2021.naacl-main)
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| Challenge: | Existing solvers for math word problems often achieve high performance on benchmark datasets . existing models rely on shallow heuristics to achieve high accuracy . |
| Approach: | They restrict their attention to English MWPs taught in grades four and lower . they propose a challenge dataset to test the accuracy of MWp solvers . |
| Outcome: | The proposed model can solve a large fraction of MWPs even with shallow heuristics . the proposed model is much lower on the challenge dataset SVAMP . |
Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems (2022.findings-acl)
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| Challenge: | Existing models memorize procedures from context and rely on shallow heuristics to solve MWPs. |
| Approach: | They propose a contrastive learning approach where the neural network perceives the divergence of patterns. |
| Outcome: | The proposed method greatly improves performance in monolingual and multilingual settings. |
Solving Math Word Problems via Cooperative Reasoning induced Language Models (2023.acl-long)
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Xinyu Zhu, Junjie Wang, Lin Zhang, Yuxiang Zhang, Yongfeng Huang, Ruyi Gan, Jiaxing Zhang, Yujiu Yang
| Challenge: | Large-scale pre-trained language models (PLMs) can be used to solve math word problems, but they lack fast adaptivity as humans. |
| Approach: | They propose a cooperative reasoning-induced PLM for solving the math word problem . they use system 1 as the generator and system 2 as the verifier to generate reasoning paths . |
| Outcome: | The proposed model improves on several mathematical reasoning datasets and achieves 9.6% improvement over baselines. |
Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems (2020.emnlp-main)
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| Challenge: | Existing models focus on one-unknown linear MWPs. |
| Approach: | They propose a universal expression tree-structured solver that integrates multiple expression trees underlying a MWP into a single expression tree. |
| Outcome: | The proposed method outperforms state-of-the-art models on a MWPs dataset and generates a universal expression tree explicitly by deciding which symbol to generate . |
Noun-MWP: Math Word Problems Meet Noun Answers (2022.coling-1)
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| Challenge: | Existing MWP solvers can handle Noun-MWPs, but they are not as efficient as other models. |
| Approach: | They propose a method to empower existing MWP solvers to handle Noun-MWPs. |
| Outcome: | The proposed model solves Noun-MWPs significantly better than other models and solves conventional MWP problems as well. |
TMATH A Dataset for Evaluating Large Language Models in Generating Educational Hints for Math Word Problems (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) are increasingly being applied in education, showing significant potential in personalized instruction, student feedback, and intelligent tutoring systems (ITSs). |
| Approach: | They propose a dataset specifically designed to evaluate LLMs’ ability to generate high-quality hints for Math Word Problems. |
| Outcome: | The proposed dataset shows that LLMs can generate more accurate and contextually appropriate educational hints for math word problems without offering direct answers. |
Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations (2021.emnlp-main)
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| Challenge: | Existing models for generating mathematical word problems are lacking in educational assessment. |
| Approach: | They propose an end-to-end neural model to generate diverse mathematical word problems from commonsense knowledge graph and equations. |
| Outcome: | The proposed model outperforms the SOTA models in terms of evaluation metrics and topic relevance. |
Graph-to-Tree Learning for Solving Math Word Problems (2020.acl-main)
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| Challenge: | Existing tree-based neural models do not capture the relationships and order information among the quantities well. |
| Approach: | They propose a novel deep learning architecture that combines the merits of the graph-based encoder and tree-based decoder to generate better solution expressions. |
| Outcome: | The proposed framework outperforms the state-of-the-art on two available datasets significantly. |
Reasoning in Large Language Models Through Symbolic Math Word Problems (2023.findings-acl)
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| Challenge: | Large language models (LLMs) have revolutionized NLP by solving downstream tasks with little to no labeled data. |
| Approach: | They propose a model that uses symbolic expressions to provide a concise explanation of the numeric answer. |
| Outcome: | The proposed model has good accuracy on symbolic word problems and is able to provide a concise and verifiable reasoning and make it more interpretable. |
Interpretable Math Word Problem Solution Generation via Step-by-step Planning (2023.acl-long)
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| Challenge: | Existing approaches to solving math word problems focus on obtaining the correct answer. |
| Approach: | They propose a step-by-step planning approach for intermediate solution generation that strategically plans the generation of the next solution step based on the MWP and the previous solution steps. |
| Outcome: | The proposed approach improves the accuracy and interpretability of the solution on automatic metrics and human evaluation. |
Instructing Large Language Models to Identify and Ignore Irrelevant Conditions (2024.naacl-long)
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| Challenge: | Existing CoT prompting methods elicited multi-step reasoning abilities of large language models (LLMs) but they were seriously confused by the irrelevant conditions, resulting in low accuracy. |
| Approach: | They propose a method that instructs large language models to identify and ignore irrelevant conditions and prompts them to verify the irrelevant conditions. |
| Outcome: | The proposed approach outperforms existing methods on MWPs with GPT-3.5-Turbo and I3C-Select. |
WARM: A Weakly (+Semi) Supervised Math Word Problem Solver (2022.coling-1)
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| Challenge: | Existing approaches to solving math word problems require full supervision in the form of intermediate equations. |
| Approach: | They propose a weakly supervised model that requires only the final answer as supervision to solve math word problems. |
| Outcome: | The proposed model achieves accuracy gains of 4.5% and 32% over current weakly-supervised methods on standard Math23K and AllArith datasets. |
Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks (2021.acl-long)
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| Challenge: | Existing solutions for math word problems lack explicit integration of math symbolic constraints, leading to unexplainable and unreasonable predictions. |
| Approach: | They propose a novel mathematical model that explicitly incorporates symbolic constraints by auxiliary tasks to enforce different symbolic reasoning. |
| Outcome: | The proposed solver incorporates symbolic constraints by auxiliary tasks to enforce different symbolic reasoning. |
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints (2021.emnlp-main)
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| Challenge: | Existing approaches to generate arithmetic math word problems are invalid or have unsatisfactory language quality. |
| Approach: | They propose a method for automatically generating arithmetic math word problems from equations and context. |
| Outcome: | The proposed approach improves language quality and mathematical validity on three real-world MWP datasets. |
Generating Pedagogically Meaningful Visuals for Math Word Problems: A New Benchmark and Analysis of Text-to-Image Models (2025.findings-acl)
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| Challenge: | Math word problems (MWPs) describe mathematical scenarios through text, requiring learners to interpret both linguistic and numerical information to derive mathematical expressions. |
| Approach: | They propose a framework for generating pedagogically meaningful visuals from MWP text descriptions using a pre-defined visual language and a design space grounded in interviews with math teachers. |
| Outcome: | The proposed framework illustrates the core mathematical relationships in math word problems. |
Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions (P19-1)
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| Challenge: | Several deep learning models have been proposed for solving math word problems (MWPs) but their approaches to capturing features are not specifically designed for MWP. |
| Approach: | They propose to use a group attention mechanism to extract global features, quantity-related features, quantities-pair features and question-related feature in MWPs. |
| Outcome: | The proposed approach performs significantly better than previous state-of-the-art methods and boosts performance from 66.9% to 69.5% on Math23K with training-test split, from 65.8% to 66.99% on Math 23K with 5-fold cross-validation and from 69.99% to 76.1% on MAWPS. |
Analogical Math Word Problems Solving with Enhanced Problem-Solution Association (2022.emnlp-main)
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| Challenge: | Analogical reasoning has long been used in mathematical education, as it enables students to apply common relational structures of mathematical situations to solve new problems. |
| Approach: | They propose to leverage analogical MWPs to advance the solver’s generalization ability across different kinds of MWps. |
| Outcome: | The proposed model has a stronger generalization ability in solving difficult MWPs due to the analogical learning from easy MWPS. |
StatsChartMWP: A Dataset for Evaluating Multimodal Mathematical Reasoning Abilities on Math Word Problems with Statistical Charts (2025.findings-emnlp)
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| Challenge: | StatsChartMWP is a dataset for evaluating visual mathematical reasoning abilities on math word problems with statistical charts. |
| Approach: | They propose a dataset for evaluating visual mathematical reasoning abilities on math word problems with statistical charts. |
| Outcome: | The proposed model is more effective than open-source approaches. |
Learning by Analogy: Diverse Questions Generation in Math Word Problem (2023.findings-acl)
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| Challenge: | Existing methods for solving math word problem (MWP) use shortcut learning to train solvers based on samples with a single question. |
| Approach: | They propose to generate diverse yet consistent questions from a common scenario . they then feed the equations to a question generator to obtain the diverse questions . their method leads to performance improvement on the current benchmark Math23K . |
| Outcome: | The proposed method generates diverse yet consistent questions with a variety of equations and questions . it improves on the current benchmark, which is based on the proposed method . |
Exposing the Achilles’ Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning (2025.acl-long)
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| Challenge: | Existing evaluations focus on final accuracy, neglecting the critical aspect of reasoning capabilities. |
| Approach: | They propose to evaluate LLMs’ abilities to detect and correct reasoning mistakes by using rule-based methods and smaller language models. |
| Outcome: | The proposed model outperforms existing models such as GPT-4o and GPT4 in both accuracy and accuracy, but lacks data contamination and memorization concerns. |
EDUMATH: Generating Standards-aligned Educational Math Word Problems (2026.acl-long)
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Bryan R Christ, Penelope Molitz, Beau LeBlond, Zachary Gottesman, Jonathan Kropko, Thomas Hartvigsen
| Challenge: | Math word problems (MWPs) are critical elements of K-12 math education and can be customized to students' interests and ability levels. |
| Approach: | They propose that LLMs can generate MWPs customized to student interests and math education standards by using an open and closed LLM to evaluate over 11,000 MWps and develop a teacher-annotated dataset for standards-aligned educational MWPS generation. |
| Outcome: | The proposed model outperforms existing closed models without training and is more similar to human-written MWPs but prefers customized MWPS with grade school students. |