Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: mit
+pretty_name: μ-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: binary
+  - 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+**U-MATH** is a comprehensive benchmark of 1,100 unpublished university-level problems sourced from real teaching materials.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 Hugginface](https://huggingface.co/datasets/toloka/umath)
+* 🔎 [μ-MATH benchmark at Hugginface](https://huggingface.co/datasets/toloka/mumath)
+* 🗞️ [Paper](LINK)
+* 👾 [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)
+
+### 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 [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]