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MathOdyssey: Benchmarking Mathematical Problem-Solving Skills in Large Language Models Using Odyssey Math Data.

Meng Fang1, Xiangpeng Wan2, Fei Lu3

  • 1Department of Computer Science, University of Liverpool, Liverpool, UK. Meng.Fang@liverpool.ac.uk.

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|August 8, 2025
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Summary
This summary is machine-generated.

A new dataset, MathOdyssey, was created to evaluate large language models (LLMs) on mathematical reasoning. This resource aids in assessing and improving LLM performance on complex math problems.

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Area of Science:

  • Artificial Intelligence
  • Mathematics Education

Background:

  • Large language models (LLMs) excel at natural language tasks but struggle with complex mathematical reasoning.
  • Evaluating LLM mathematical capabilities requires specialized datasets for rigorous assessment.

Purpose of the Study:

  • Introduce MathOdyssey, a novel dataset for evaluating mathematical reasoning in LLMs.
  • Provide a standardized resource for reproducible LLM performance assessment in mathematics.

Main Methods:

  • Curated 387 expert-generated mathematical problems from high school to Olympiad levels.
  • Included detailed solutions and categorized problems by difficulty, subject, and answer type.
  • Developed dataset through expert contributions, peer review, and standardized formatting.

Main Results:

  • Evaluated performance of representative LLMs on the MathOdyssey dataset.
  • Reported LLM performance across various problem types and difficulty levels.

Conclusions:

  • MathOdyssey serves as an open-access resource for fine-grained assessment of LLM mathematical abilities.
  • The dataset will foster research in AI-driven mathematical reasoning and education.