BeyondX

Dataset Card for BeyondX

• Dataset Description
• Paper Information
• Dataset Usage
• Data Construction
• Formulate-and-Solve
• License
• Citation

Dataset Description

BeyondX is a novel algebra reasoning benchmark within multi-unknown, which addresses the limitation that problems with at most two unknowns dominate existing math datasets. In total, BeyondX includes 464 examples generated from 2 different source datasets.

Paper Information

• Project Page: https://johnsonkao0213.github.io/Formulate_and_Solve/
• Paper: https://arxiv.org/abs/2407.05134
• Code: https://github.com/johnsonkao0213/Formulate_and_Solve
• Visualization: https://johnsonkao0213.github.io/Formulate_and_Solve/#visualization
• Leaderboard: https://johnsonkao0213.github.io/Formulate_and_Solve/#leaderboard

Dataset Usage

Data Construction

The BeyondX dataset is derived from two collected datasets: ALG514 and DRAW-1K. To efficiently generate a large corpus of multi-unknown problems, we developed a novel pipeline that automatically expands existing problems to N unknowns, please refer to our GitHub repository and Paper for details here.

Formulate-and-Solve

🔔 To evaluate our proposed SoTA prompting method Formulate-and-Solve on BeyondX, please refer to our GitHub repository and Paper for details here.

License

The new contributions to our dataset are distributed under the CC BY-SA 4.0 license.

The copyright of the questions belongs to the original authors, and the source of every original question can be found in the source field. Alongside this license, the following conditions apply:

• Purpose: The dataset was primarily designed as a test set.
• Commercial Use: The dataset can be used commercially as a test set, but using it as a training set is prohibited. By accessing or using this dataset, you acknowledge and agree to abide by these terms in conjunction with the CC BY-SA 4.0 license.

Citation

@misc{kao2024solvingxbeyondlarge,
      title={Solving for X and Beyond: Can Large Language Models Solve Complex Math Problems with More-Than-Two Unknowns?}, 
      author={Kuei-Chun Kao and Ruochen Wang and Cho-Jui Hsieh},
      year={2024},
      eprint={2407.05134},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2407.05134}, 
}