Challenge: Existing studies show that LLMs struggle with text interpretation and equation solving, despite distinct proficiencies in textual and mathematical components.
Approach: They disentangle textual interpretation and mathematical solving steps in word problems drawn from Brazil's largest college entrance exam and popular grade school-level benchmark GSM8K.
Outcome: The proposed model outperforms LLMs in Brazil's largest college entrance exam and popular grade school-level benchmark.

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Large Language Models for Mathematical Reasoning: Progresses and Challenges (2024.eacl-srw)

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Challenge: a survey examines the landscape of mathematical problem-solving techniques . large language models have proven to be potent assets in unraveling nuances of mathematical reasoning .
Approach: They examine the evolution of Large Language Models (LLMs) for solving mathematical problems . they examine the spectrum of LLM-oriented techniques proposed for solving math problems - and their challenges .
Outcome: The survey examines the spectrum of proposed LLM-oriented techniques in solving math problems.
LLMs for Mathematical Modeling: Towards Bridging the Gap between Natural and Mathematical Languages (2025.findings-naacl)

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Challenge: Large Language Models (LLMs) have demonstrated strong performance across various natural language processing tasks, but their proficiency in mathematical reasoning remains a key challenge.
Approach: They propose a process-oriented framework to evaluate LLMs' ability to construct mathematical models, using solvers to compare outputs with ground truth.
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Can Large Language Models Win the International Mathematical Games? (2025.emnlp-main)

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Challenge: Recent advances in large language models (LLMs) have demonstrated strong mathematical reasoning abilities, even in visual contexts.
Approach: They propose a benchmark of 2,183 high-quality mathematical problems in an open-ended format that enables a structured evaluation of LLMs’ mathematical and logical reasoning abilities.
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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.
From A and B to A+B: Can Large Language Models Solve Compositional Math Problems? (2025.emnlp-main)

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Challenge: Existing studies that create problem variants by adding perturbations to a single problem focus on the interaction between problems.
Approach: They propose a pipeline with 98.2% accuracy to combine two original problems with a logical connection and to evaluate LLMs' generalization ability on the compositional problems.
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LLM Parameters for Math Across Languages: Shared or Separate? (2026.acl-srw)

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Challenge: Existing research on large language models (LLMs) has focused on performance or representational properties, but it remains unclear whether these differences reflect language-specific parameters or a shared mechanism.
Approach: They propose to localize and compare model parameters that support mathematical reasoning across languages.
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Rationales for Answers to Simple Math Word Problems Confuse Large Language Models (2024.findings-acl)

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Challenge: Recent studies show that large language models have advanced mathematical problem-solving abilities in grade school math word problems.
Approach: They propose to combine fine-tuning and prompt-based methods to improve performance . they propose to use a hybrid algorithm to fine- tune LLMs on specific tasks .
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1,729 vs. 1729: The Effect of Scripts and Formats on LLM Numeracy (2026.findings-acl)

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Challenge: Large language models (LLMs) have impressive proficiency in basic arithmetic, but little attention has been given to how they perform when numerical expressions deviate from the prevailing conventions present in their training corpora.
Approach: They investigate numerical reasoning across a wide range of numeral scripts and formats . they show that LLM accuracy drops substantially when numerical inputs are rendered in underrepresented scripts or formats despite the underlying mathematical reasoning being identical .
Outcome: The proposed methods can narrow the gap between LLMs and human models when they deviate from prevailing numerical conventions.
Do LLMs Overthink Basic Math Reasoning? Benchmarking the Accuracy-Efficiency Tradeoff in Language Models (2026.findings-acl)

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Challenge: Large language models (LLMs) achieve impressive performance on complex benchmarks yet sometimes fail on basic math reasoning.
Approach: They propose a benchmark to evaluate the efficiency of reasoning in large language models . they formalize the accuracy-verbosity tradeoff and introduce the overthinking score .
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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.

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