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metadata
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}
}