toloka/u-math

U-MATH is a comprehensive benchmark of 1,100 unpublished university-level problems sourced from real teaching materials.

It is designed to evaluate the mathematical reasoning capabilities of Large Language Models (LLMs).
The dataset is balanced across six core mathematical topics and includes 20% of multimodal problems (involving visual elements such as graphs and diagrams).

For fine-grained performance evaluation results and detailed discussion, check out our paper.

• 📊 U-MATH benchmark at Huggingface
• 🔎 μ-MATH benchmark at Huggingface
• 🗞️ Paper
• 👾 Evaluation Code at GitHub

Key Features

• Topics Covered: Precalculus, Algebra, Differential Calculus, Integral Calculus, Multivariable Calculus, Sequences & Series.
• Problem Format: Free-form answer with LLM judgement
• Evaluation Metrics: Accuracy; splits by subject and text-only vs multimodal problem type.
• Curation: Original problems composed by math professors and used in university curricula, samples validated by math experts at Toloka AI, Gradarius

Use it

from datasets import load_dataset
ds = load_dataset('toloka/u-math', split='test')

Dataset Fields

uuid: problem id
has_image: a boolean flag on whether the problem is multimodal or not
image: binary data encoding the accompanying image, empty for text-only problems
subject: subject tag marking the topic that the problem belongs to
problem_statement: problem formulation, written in natural language
golden_answer: a correct solution for the problem, written in natural language \

For meta-evaluation (evaluating the quality of LLM judges), refer to the µ-MATH dataset.

Evaluation Results

The prompt used for inference:

{problem_statement}
Please reason step by step, and put your final answer within \boxed{}

Licensing Information

All the dataset contents are available under the MIT license.

Citation

If you use U-MATH or μ-MATH in your research, please cite the paper:

@inproceedings{umath2024,
title={U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMs},
author={Konstantin Chernyshev, Vitaliy Polshkov, Ekaterina Artemova, Alex Myasnikov, Vlad Stepanov, Alexei Miasnikov and Sergei Tilga},
year={2024}
}

Contact

For inquiries, please contact kchernyshev@toloka.ai