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MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?
European Conference on Computer VisionThe remarkable progress of Multi-modal Large Language Models (MLLMs) has garnered unparalleled attention, due to their superior performance in visual contexts. However, their capabilities in visual math problem-solving remain insufficiently evaluated and
Renrui Zhang +10 more
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Experimental Math for Math Monthly Problems
The American Mathematical Monthly, 2017Experimental mathematics is a newly developed approach to discovering mathematical truths through the use of computers.
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We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?
arXiv.orgVisual mathematical reasoning, as a fundamental visual reasoning ability, has received widespread attention from the Large Multimodal Models (LMMs) community.
Runqi Qiao +17 more
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Science
A false “equity versus excellence” debate over mathematics curricula has long disrupted education in the United ...
Alan, Schoenfeld, Phil, Daro
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A false “equity versus excellence” debate over mathematics curricula has long disrupted education in the United ...
Alan, Schoenfeld, Phil, Daro
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Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning
arXiv.orgMath reasoning has become the poster child of progress in large language models (LLMs), with new models rapidly surpassing human-level performance on benchmarks like MATH and AIME.
M. Huan +8 more
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MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations
International Conference on Machine LearningLarge language models have demonstrated impressive performance on challenging mathematical reasoning tasks, which has triggered the discussion of whether the performance is achieved by true reasoning capability or memorization.
Kaixuan Huang +17 more
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Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models
arXiv.orgIncreasing interest in reasoning models has led math to become a prominent testing ground for algorithmic and methodological improvements. However, existing open math datasets either contain a small collection of high-quality, human-written problems or a
Alon Albalak +10 more
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MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
North American Chapter of the Association for Computational Linguistics, 2019We introduce a large-scale dataset of math word problems and an interpretable neural math problem solver by learning to map problems to their operation programs.
Aida Amini +5 more
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OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset
Neural Information Processing SystemsRecent work has shown the immense potential of synthetically generated datasets for training large language models (LLMs), especially for acquiring targeted skills. Current large-scale math instruction tuning datasets such as MetaMathQA (Yu et al., 2024)
Shubham Toshniwal +5 more
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Interactive Technology and Smart Education, 2006
Educators recognize that group work and physical involvement with learning materials can greatly enhance the understanding and retention of difficult concepts. As a result, math manipulatives ‐ such as pattern blocks and number lines ‐ have increasingly been making their way into classrooms and children’s museums. Yet without the constant guidance of a
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Educators recognize that group work and physical involvement with learning materials can greatly enhance the understanding and retention of difficult concepts. As a result, math manipulatives ‐ such as pattern blocks and number lines ‐ have increasingly been making their way into classrooms and children’s museums. Yet without the constant guidance of a
openaire +1 more source