dataset_info:
features:
- name: id
dtype: int64
- name: subfield
dtype: string
- name: context
dtype: 'null'
- name: problem
dtype: string
- name: solution
dtype: string
- name: final_answer
sequence: string
- name: is_multiple_answer
dtype: bool
- name: unit
dtype: string
- name: answer_type
dtype: string
- name: error
dtype: string
- name: original_solution
sequence: string
splits:
- name: all_data
num_bytes: 3030627
num_examples: 825
- name: train
num_bytes: 2571441.090909091
num_examples: 700
- name: test
num_bytes: 459185.9090909091
num_examples: 125
download_size: 2837889
dataset_size: 6061254
configs:
- config_name: default
data_files:
- split: all_data
path: data/all_data-*
- split: train
path: data/train-*
- split: test
path: data/test-*
extra_gated_prompt: >-
By requesting access to this dataset, you agree to cite the following works in
any publications or projects that utilize this data:
- Putnam-AXIOM dataset: @article{putnam_axiom2024, title={Putnam-AXIOM: A
Functional and Static Benchmark for Measuring Higher Level Mathematical
Reasoning}, author={Aryan Gulati and Brando Miranda and Eric Chen and Emily
Xia and Kai Fronsdal and Bruno de Moraes Dumont and Sanmi Koyejo},
journal={38th Conference on Neural Information Processing Systems (NeurIPS
2024) Workshop on MATH-AI}, year={2024},
url={https://openreview.net/pdf?id=YXnwlZe0yf}, note={Preprint available at:
https://openreview.net/pdf?id=YXnwlZe0yf}}
- OlympiadBench dataset (if applicable): @misc{he2024olympiadbench,
title={OlympiadBench: A Challenging Benchmark for Promoting AGI with
Olympiad-Level Bilingual Multimodal Scientific Problems}, author={Chaoqun He
and Renjie Luo and others}, year={2024}, eprint={2402.14008},
archivePrefix={arXiv}, primaryClass={cs.CL}}
OlympiadBench Data set used in the Putnam-AXIOM Paper
The Putnam-AXIOM dataset is a benchmark for measuring advanced mathematical reasoning in large language models (LLMs).
It includes challenging mathematical problems from the William Lowell Putnam Mathematical Competition, with both original problems and functional variations to address data contamination. The dataset aims to provide rigorous evaluations by requiring models to answer in boxed format, simplifying automatic answer matching.
OlympiadBench is an Olympiad level Benchmark with IMO questions.
We added the latex box (\\boxed{$\\frac{1}{2}$}"
) for compatibility with Hendrycks's standard evaluations.
Dataset Details
- Title: OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems
- Paper: Available at arxiv
- Number of Problems: 825
TODO: transform image into Asymptote language like this https://arxiv.org/pdf/2103.03874
Usage
The dataset can be loaded in Python with the following code:
from datasets import load_dataset
dataset = load_dataset("brando/olympiad-bench-imo-math-boxed-21-08-02024-v2", split='all')
Legal Compliance TODO
Citation If you use this dataset, please cite:
Copy code
@article{putnam_axiom2024,
title = {Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning},
author = {Aryan Gulati and Brando Miranda and Eric Chen and Emily Xia and Kai Fronsdal and Bruno de Moraes Dumont and Sanmi Koyejo},
journal = {38th Conference on Neural Information Processing Systems (NeurIPS 2024) Workshop on MATH-AI},
year = {2024},
url = {https://openreview.net/pdf?id=YXnwlZe0yf},
note = {Preprint available at: https://openreview.net/pdf?id=YXnwlZe0yf}
}
Cite the original work please:
@misc{he2024olympiadbench,
title={OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems},
author={Chaoqun He and Renjie Luo and Yuzhuo Bai and Shengding Hu and Zhen Leng Thai and Junhao Shen and Jinyi Hu and Xu Han and Yujie Huang and Yuxiang Zhang and Jie Liu and Lei Qi and Zhiyuan Liu and Maosong Sun},
year={2024},
eprint={2402.14008},
archivePrefix={arXiv},
primaryClass={cs.CL}
}