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---
license: mit
pretty_name: U-MATH
task_categories:
- text-generation
language:
- en
tags:
- math
- reasoning
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: uuid
dtype: string
- name: subject
dtype: string
- name: has_image
dtype: bool
- name: image
dtype: string
- name: problem_statement
dtype: string
- name: golden_answer
dtype: string
splits:
- name: test
num_examples: 1100
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
**U-MATH** is a comprehensive benchmark of 1,100 unpublished university-level problems sourced from real teaching materials.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](LINK).
* 📊 [U-MATH benchmark at Huggingface](https://huggingface.co/datasets/toloka/umath)
* 🔎 [μ-MATH benchmark at Huggingface](https://huggingface.co/datasets/toloka/mumath)
* 🗞️ [Paper](https://arxiv.org/abs/2412.03205)
* 👾 [Evaluation Code at GitHub](https://github.com/Toloka/u-math/)
### 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](https://toloka.ai), [Gradarius](https://www.gradarius.com)
### Use it
```python
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](https://huggingface.co/datasets/toloka/mu-math).
### Evaluation Results
<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/650238063e61bc019201e3e2/beMyOikpKfp3My2vu5Mjc.png" alt="umath-table" width="800"/>
</div>
<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/650238063e61bc019201e3e2/7_VZXidxMHG7PiDM983lS.png" alt="umath-bar" width="950"/>
</div>
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:
```bibtex
@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 [email protected] |