README.md

---
Paper: arxiv.org/abs/2406.16772
license: cc-by-nc-sa-4.0
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- config_name: CS
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configs:
- config_name: Math
  data_files:
  - split: test
    path: Math/test-*
  - split: val
    path: Math/val-*
- config_name: Physics
  data_files:
  - split: test
    path: Physics/test-*
  - split: val
    path: Physics/val-*
- config_name: Chemistry
  data_files:
  - split: test
    path: Chemistry/test-*
  - split: val
    path: Chemistry/val-*
- config_name: Biology
  data_files:
  - split: test
    path: Biology/test-*
  - split: val
    path: Biology/val-*
- config_name: Geography
  data_files:
  - split: test
    path: Geography/test-*
  - split: val
    path: Geography/val-*
- config_name: Astronomy
  data_files:
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    path: Astronomy/test-*
  - split: val
    path: Astronomy/val-*
- config_name: CS
  data_files:
  - split: test
    path: CS/test-*
  - split: val
    path: CS/val-*
task_categories:
- question-answering
language:
- en
- zh
pretty_name: OlympicArena
size_categories:
- 10K<n<100K
tags:
- croissant
---


# OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI

**OlympicArena** is a comprehensive, highly-challenging, and rigorously curated benchmark featuring a detailed, fine-grained evaluation mechanism designed to assess advanced AI capabilities across a broad spectrum of Olympic-level challenges.

This benchmark encompasses seven disciplines: Mathematics, Physics, Chemistry, Biology, Geography, Astronomy, and Computer Science. Each discipline is divided into two splits: validation (val) and test. The validation split includes publicly available answers for small-scale testing and evaluation, while the test split does not disclose the answers, users could submit their results.


# An Example to load the data

```python
from datasets import load_dataset
dataset=load_dataset("GAIR/OlympicArena", "Math", split="val")

print(dataset[0])
```

More details on loading and using the data are at our [github page](https://github.com/GAIR-NLP/OlympicArena).