Datasets:

mkieffer
/

M-ARC

dataset_info:
  features:
    - name: question_id
      dtype: string
    - name: question
      dtype: string
    - name: options
      struct:
        - name: A
          dtype: string
        - name: B
          dtype: string
        - name: C
          dtype: string
        - name: D
          dtype: string
        - name: E
          dtype: string
        - name: F
          dtype: string
        - name: G
          dtype: string
    - name: answer
      dtype: string
    - name: src
      dtype: string
  splits:
    - name: test
      num_bytes: 109817
      num_examples: 100
  download_size: 49322
  dataset_size: 109817
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*
license: apache-2.0

M-ARC

HuggingFace upload of a clinical QA benchmark designed to exploit LLMs' "inductive biases toward inflexible pattern matching from their training data rather than engaging in flexible reasoning." If used, please cite the original authors using the citation below.

Dataset Details

Dataset Description

The dataset contains one split:

test: up to seven-option multiple-choice QA (choices A-G)

Dataset Sources

Repository: https://github.com/dbernardo05/medARC-QA
Paper: https://arxiv.org/pdf/2502.04381

Direct Use

import json
from datasets import load_dataset

if __name__ == "__main__":
    # load the test split
    dataset_test = load_dataset("mkieffer/M-ARC", split="test")
    print("\test split:\n", dataset_test)
    print("\ntest sample:\n", json.dumps(dataset_test[0], indent=2))

Citation

@misc{kim2025limitationslargelanguagemodels,
      title={Limitations of Large Language Models in Clinical Problem-Solving Arising from Inflexible Reasoning}, 
      author={Jonathan Kim and Anna Podlasek and Kie Shidara and Feng Liu and Ahmed Alaa and Danilo Bernardo},
      year={2025},
      eprint={2502.04381},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.04381}, 
}