Papers with math word problems
Neural Math Word Problem Solver with Reinforcement Learning (C18-1)
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| Challenge: | Existing models for solving math word problems rely on predefined rules or feature engineering. |
| Approach: | They propose to incorporate copy and alignment mechanism into the sequence-to-sequence model to address two shortcomings . they use model output as a feature and incorporate it into the feature-based model to explore the effectiveness . |
| Outcome: | The proposed model outperforms the state-of-the-art models on the problem solving task. |
A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models (2023.acl-long)
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| Challenge: | Recent work shows that language models can rely on shallow patterns in problem description when generating a solution. |
| Approach: | They propose a framework which pins down the causal effect of various factors on the output solution. |
| Outcome: | The proposed framework improves robustness and sensitivity to direct interventions on a test bed of math word problems. |
Using Intermediate Representations to Solve Math Word Problems (P18-1)
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| Challenge: | Existing approaches to solving math word problems do not include higher-order operations that cannot be explicitly represented in equations. |
| Approach: | They propose an iterative labeling framework that generates intermediate forms and executes them to obtain the final answers. |
| Outcome: | The proposed model outperforms existing models in solving math word problems. |
MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning (2024.naacl-long)
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| Challenge: | TALMs have been successfully employed in question-answering benchmarks, but their efficacy on complex mathematical reasoning benchmarks are open research questions. |
| Approach: | They propose a tool-augmented large language model for mathematical reasoning that enhances the skillset of large language models (LLMs) by 13.5%. |
| Outcome: | The proposed model achieves better accuracy and better knowledge retrieval performance than existing tools. |
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. |
Towards Generating Controllable and Solvable Geometry Problem by Leveraging Symbolic Deduction Engine (2025.acl-industry)
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Zhuoxuan Jiang, Tianyang Zhang, Peiyan Peng, Jing Chen, Yinong Xun, Haotian Zhang, Lichi Li, Yong Li, Shaohua Zhang
| Challenge: | Compared to math word problems, geometry problems emphasize multi-modal formats and the translation between informal and formal languages. |
| Approach: | They propose a symbolic deduction engine-based geometry problem generation framework that leverages a symbolic deduction engine to generate geometry problems. |
| Outcome: | The proposed method avoids inherent biases in translating natural language into formal language and guarantees to control the generated problems in terms of knowledge points and difficulties by an elaborate checking function. |
Sequence to General Tree: Knowledge-Guided Geometry Word Problem Solving (2021.acl-short)
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| Challenge: | Existing neural solvers only generate binary expression trees that contain basic arithmetic operators and do not explicitly use the math formulas. |
| Approach: | They propose a sequence-to-general tree that generates interpretable and executable operation trees where nodes can be formulas with an arbitrary number of arguments. |
| Outcome: | The proposed tree generates interpretable and executable operation trees with formulas with an arbitrary number of arguments. |
An Edge-Enhanced Hierarchical Graph-to-Tree Network for Math Word Problem Solving (2021.findings-emnlp)
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| Challenge: | Existing work on graph neural networks to capture word relationships neglects the rest of the problem. |
| Approach: | They propose an edge-enhanced hierarchical graph encoder to incorporate edge label information. |
| Outcome: | The proposed model can improve performance on the MAWPS and Math23K datasets compared with state-of-the-art methods. |
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 . |
ComSearch: Equation Searching with Combinatorial Strategy for Solving Math Word Problems with Weak Supervision (2023.eacl-main)
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| Challenge: | Existing weakly-supervised methods for solving math word problems are expensive and time-consuming. |
| Approach: | They propose a weakly-supervised approach to solve math word problems . they propose 'comsearch' algorithm which compresses the search space by excluding mathematically equivalent equations. |
| Outcome: | The proposed algorithm can compress the search space by excluding mathematically equivalent equations. |
Tree-structured Decoding for Solving Math Word Problems (D19-1)
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| Challenge: | Existing approaches to solve math word problems do not consider an abstract syntax tree. |
| Approach: | They propose a tree-structured decoding method that generates an abstract syntax tree of an equation in a top-down manner and can stop during decoding without a redundant stop token. |
| Outcome: | The proposed method achieves state-of-the-art performance on the largest dataset on this task. |
MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms (N19-1)
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| Challenge: | Existing datasets in this domain do not offer precise operational annotations over diverse problem types due to noise and lack of formal operation-based representations. |
| Approach: | They propose a representation language to map problems to their operation programs . they also introduce an interpretable neural math problem solver . |
| Outcome: | The proposed model outperforms baseline models and the AQUA-RAT dataset on the AQuA-rat dataset. |
Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems (N19-1)
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| Challenge: | Existing methods to solve math word problems require accurate natural language understanding to bridge texts and math expressions. |
| Approach: | They propose a neural approach to automatically solve math word problems by operating symbols according to their semantic meanings in texts. |
| Outcome: | The proposed model outperforms state-of-the-art models and the best non-retrieval-based models over 10% accuracy in a Math23K dataset. |
Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning (2021.emnlp-main)
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| Challenge: | Existing algorithms for math word problems only capture word-level relationship and ignore to build hierarchical reasoning like the human being. |
| Approach: | They propose a Reasoning with Pre-trained Knowledge and Hierarchical Structure network that uses outside knowledge to build hierarchical reasoning like the human being. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two large-scale datasets and boosts performance. |
Adversarial Math Word Problem Generation (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have revolutionized the educational landscape due to the great improvements in their natural language generation and problem-solving capabilities. |
| Approach: | They propose a cost-effective approach to attack large language models using abstract syntax trees to generate adversarial examples that preserve the structure and difficulty of the original questions aimed for assessment. |
| Outcome: | The proposed approach significantly degrades students' math problem-solving ability on open- and closed-source LLMs. |
Mapping probability word problems to executable representations (2021.emnlp-main)
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Simon Suster, Pieter Fivez, Pietro Totis, Angelika Kimmig, Jesse Davis, Luc de Raedt, Walter Daelemans
| Challenge: | a recent paper addresses the problem of solving math word problems automatically . a number of approaches have been proposed for solving word problems . |
| Approach: | They employ a sequence-to-sequence model to generate intermediate representations for word problems . they then use a probabilistic programming system to provide the answer . their best performing model incorporates general-domain contextualised word representations . |
| Outcome: | The proposed model is the best performing on a declarative language and a probabilistic programming system. |
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. |
LLMs cannot spot math errors, even when allowed to peek into the solution (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) demonstrate impressive performance on existing reasoning benchmarks, but struggle with meta-reasoning tasks such as locating the first error step in student solutions. |
| Approach: | They propose an approach that generates an intermediate corrected student solution, aligning more closely with the original student’s solution, which helps improve performance. |
| Outcome: | The proposed approach generates an intermediate corrected student solution, aligning more closely with the original student’s solution, which helps improve performance. |
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. |
ControlMath: Controllable Data Generation Promotes Math Generalist Models (2024.emnlp-main)
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| Challenge: | Currently, mathematical reasoning is one of the most challenging areas for closed-source LLMs. |
| Approach: | They propose an iterative method involving an equation-generator module and two LLM-based agents that generate diverse equations and transform them into math word problems. |
| Outcome: | The proposed method enables the generation of diverse math problems, not limited to specific domains or distributions. |
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. |
Can LLMs Reason Abstractly Over Math Word Problems Without CoT? Disentangling Abstract Formulation From Arithmetic Computation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are often evaluated on math word problems . however, such metrics conflate two distinct sub-skills: abstract formulation and arithmetic computation. |
| Approach: | They propose to use Final-answer-based metrics to evaluate large language models on math word problems to conflate two distinct sub-skills: abstract formulation and arithmetic computation. |
| Outcome: | The proposed model performance is bottlenecked by arithmetic computation and not abstract formulation, the study shows. |
TabularMath: Understanding Math Reasoning over Tables with Large Language Models (2026.findings-acl)
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| Challenge: | Mathematical reasoning has long been a key benchmark for evaluating large language models. |
| Approach: | They propose a framework that transforms math word problems into scalable tabular reasoning tasks. |
| Outcome: | The proposed framework transforms math word problems into scalable and verified tabular reasoning tasks. |
NUMCoT: Numerals and Units of Measurement in Chain-of-Thought Reasoning using Large Language Models (2024.findings-acl)
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| Challenge: | Existing LLMs are not able to handle numerals and units of measurement, but they can be improved by introducing perturbations. |
| Approach: | They propose to analyze existing LLMs on processing numerals and units of measurement by perturbing their datasets. |
| Outcome: | The proposed model improves on ancient Chinese arithmetic problems and can handle numeral and measurement conversions. |
Reasoning Paths Optimization: Learning to Reason and Explore From Diverse Paths (2024.findings-emnlp)
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| Challenge: | Advanced models such as OpenAI o1 exhibit impressive problem-solving capabilities, but they may still falter on more complex problems, making errors that disrupt their reasoning paths. |
| Approach: | They propose a framework that encourages favorable branches at each reasoning step while penalizing unfavorable ones, enhancing the model’s overall problem-solving performance. |
| Outcome: | The proposed framework improves reasoning performance on multi-step reasoning tasks such as math word problems and science-based exam questions. |
Uncovering the Impact of Chain-of-Thought Reasoning for Direct Preference Optimization: Lessons from Text-to-SQL (2025.acl-long)
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| Challenge: | Direct Preference Optimization (DPO) is effective in complex reasoning tasks like math word problems and code generation, but Text-to-SQL datasets often include only final answers (gold SQL queries) without detailed CoT solutions. |
| Approach: | They found that Direct Preference Optimization (DPO) is crucial for unlocking DPO's potential by augmenting Text-to-SQL datasets with synthetic CoT solutions. |
| Outcome: | The proposed method achieves consistent and significant performance improvements on Text-to-SQL datasets. |
Exploring the Hidden Reasoning Process of Large Language Models by Misleading Them (2025.findings-emnlp)
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Guanyu Chen, Peiyang Wang, Yizhou Jiang, Yuqian Liu, Chujie Zhao, Ying Fang, Tianren Zhang, Feng Chen
| Challenge: | Existing large language models can perform abstract reasoning tasks but are they actually engaging in rule-based reasoning beyond mere memorization? |
| Approach: | They propose a method to examine whether large language models perform abstract reasoning . they fine-tune the model to learn those contradictory rules and assess its generalization ability . |
| Outcome: | The proposed approach examines whether large language models perform abstract reasoning by altering their original understanding of fundamental rules. |