TMATH A Dataset for Evaluating Large Language Models in Generating Educational Hints for Math Word Problems (2025.coling-main)
Copied to clipboard
| 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. |
Similar Papers
HighMATH: Evaluating Math Reasoning of Large Language Models in Breadth and Depth (2025.findings-emnlp)
Copied to clipboard
Yan Liu, Minghui Zhang, Bojian Xiong, Yifan Xiao, Yinong Sun, Yating Mei, Longyu Zeng, Jingchao Yang, Yang Wang, Deyi Xiong
| Challenge: | a gap in math models' accuracy has been widened with the development of large language models (LLMs) . a new study aims to bridge this gap by evaluating a set of high-level math reasoning models . |
| Approach: | They propose to evaluate large language models on existing math benchmarks to bridge this gap . they collect 5,293 problems from Chinese senior high school mathematics exams . |
| Outcome: | The proposed model is based on o1-like models and a high-level model. |
Rationales for Answers to Simple Math Word Problems Confuse Large Language Models (2024.findings-acl)
Copied to clipboard
| 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 . |
| Outcome: | The proposed methods improve performance on the proposed reasoning process evaluation benchmarks. |
ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models (2024.findings-acl)
Copied to clipboard
Yanan Wu, Jie Liu, Xingyuan Bu, Jiaheng Liu, Zhanhui Zhou, Yuanxing Zhang, Chenchen Zhang, ZhiqiBai ZhiqiBai, Haibin Chen, Tiezheng Ge, Wanli Ouyang, Wenbo Su, Bo Zheng
| Challenge: | ConceptMath evaluates concept-wise mathematical reasoning of Large Language Models (LLMs) Existing benchmarks that evaluate general mathematical reasoning with an average accuracy fail to probe the fine-grained failure modes of mathematical reasoning on specific datasets. |
| Approach: | They introduce a bilingual, fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models. |
| Outcome: | The proposed benchmarks evaluate concept-wise mathematical reasoning of Large Language Models with concept-based accuracies. |
It Ain’t Over: A Multi-aspect Diverse Math Word Problem Dataset (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies lack diversity in problem types, lexical usage patterns, languages, and intermediate solution forms for the math word problem. |
| Approach: | They propose a new MWP dataset with a wide range of diversity in problem types, lexical usage patterns, languages, and intermediate solutions. |
| Outcome: | The proposed dataset provides an opportunity to evaluate the capability of large language models. |
EDUMATH: Generating Standards-aligned Educational Math Word Problems (2026.acl-long)
Copied to clipboard
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. |
Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | After the pandemic, e-learning has become part of mainstream education. |
| Approach: | They propose to integrate large language models (LLMs) into educational settings to enhance students' mathematical problem-solving skills by providing adaptive feedback. |
| Outcome: | The proposed model can generate free-text rationalizations and misinterpret meanings and can also misinterprét students' answers. |
Large Language Models for Mathematical Reasoning: Progresses and Challenges (2024.eacl-srw)
Copied to clipboard
| 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. |
Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches for distilling large language models into smaller, more efficient student models are based on educational science principles such as knowledge tracing and personalized learning. |
| Approach: | They propose a method for distilling large language models into smaller, more efficient student models that are aligned with educational science principles such as knowledge tracing and personalized learning. |
| Outcome: | The proposed approach outperforms LLMs on three benchmarks while employing significantly fewer parameters. |
PyraMathBench: Evaluating and Improving Mathematical Capability in Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Numerical reasoning is ubiquitous in scientific research and financial analysis, but few benchmarks evaluate them by integrating numerical processing and mathematical reasoning. |
| Approach: | They propose a numerically-integrated hierarchical benchmark with 27,215 questions derived from 7,404 math word problems that spans 4 key cognitive aspects, 14 subcategories, and 2 modalities. |
| Outcome: | The proposed model improves Qwen-2.5 score with SOLVE and IRPO training. |
What Makes Math Word Problems Challenging for LLMs? (2024.findings-naacl)
Copied to clipboard
| 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. |