Datasets:
metadata
Paper: arxiv.org/abs/2406.16772
license: cc-by-nc-sa-4.0
dataset_info:
- config_name: Math
features:
- name: id
dtype: string
- name: problem
dtype: string
- name: prompt
dtype: string
- name: figure_urls
sequence: string
- name: answer
sequence: string
- name: solution
dtype: string
- name: answer_type
dtype: string
- name: unit
sequence: string
- name: answer_sequence
sequence: string
- name: type_sequence
sequence: string
- name: test_cases
sequence:
- name: input
dtype: string
- name: output
dtype: string
- name: subject
dtype: string
- name: language
dtype: string
- name: modality
dtype: string
splits:
- name: test
num_bytes: 3153019
num_examples: 2977
- name: val
num_bytes: 484904
num_examples: 244
download_size: 1402261
dataset_size: 3637923
- config_name: Physics
features:
- name: id
dtype: string
- name: problem
dtype: string
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dtype: string
- name: figure_urls
sequence: string
- name: answer
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sequence: string
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sequence: string
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sequence: string
- name: test_cases
sequence:
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- name: language
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splits:
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- name: val
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num_examples: 90
download_size: 1613993
dataset_size: 3422993
- config_name: Chemistry
features:
- name: id
dtype: string
- name: problem
dtype: string
- name: prompt
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- name: figure_urls
sequence: string
- name: answer
sequence: string
- name: solution
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sequence: string
- name: answer_sequence
sequence: string
- name: type_sequence
sequence: string
- name: test_cases
sequence:
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- name: language
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splits:
- name: test
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- name: val
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num_examples: 65
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- config_name: Biology
features:
- name: id
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- name: problem
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- name: val
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- config_name: Geography
features:
- name: id
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dtype: string
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- name: val
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- config_name: Astronomy
features:
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- name: test_cases
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splits:
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- name: val
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num_examples: 90
download_size: 1685604
dataset_size: 3482218
- config_name: CS
features:
- name: id
dtype: string
- name: problem
dtype: string
- name: prompt
dtype: string
- name: figure_urls
sequence: string
- name: answer
sequence: string
- name: solution
dtype: string
- name: answer_type
dtype: string
- name: unit
sequence: string
- name: answer_sequence
sequence: string
- name: type_sequence
sequence: string
- name: test_cases
sequence:
- name: input
dtype: string
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dtype: string
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dtype: string
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dtype: string
splits:
- name: test
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- name: val
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num_examples: 18
download_size: 256590378
dataset_size: 498074582
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:
- split: test
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
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.
If you do find our code helpful or use our benchmark dataset, please citing our paper.
@article{huang2024olympicarena,
title={OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI},
author={Zhen Huang and Zengzhi Wang and Shijie Xia and Xuefeng Li and Haoyang Zou and Ruijie Xu and Run-Ze Fan and Lyumanshan Ye and Ethan Chern and Yixin Ye and Yikai Zhang and Yuqing Yang and Ting Wu and Binjie Wang and Shichao Sun and Yang Xiao and Yiyuan Li and Fan Zhou and Steffi Chern and Yiwei Qin and Yan Ma and Jiadi Su and Yixiu Liu and Yuxiang Zheng and Shaoting Zhang and Dahua Lin and Yu Qiao and Pengfei Liu},
year={2024},
journal={arXiv preprint arXiv:2406.12753},
url={https://arxiv.org/abs/2406.12753}
}