---
dataset_info:
- config_name: full_agreement
  features:
  - name: dataset
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  - name: task
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  - name: score_label
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  - name: score_chosen
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  - name: score_rejected
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  - name: chosen
    list:
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    - name: role
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  - name: rejected
    list:
    - name: content
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    - name: role
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  - name: novelty_chosen
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  - name: surprise_chosen
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  - name: diversity_chosen
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  - name: quality_chosen
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  splits:
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    num_bytes: 39599062
    num_examples: 42058
  - name: val
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    num_examples: 4880
  - name: test
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    num_examples: 4533
  - name: heldout_item
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  - name: heldout_task
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    num_examples: 52
  - name: val_sample1024
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  - name: val_sample4096
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  - name: test_sample1024
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    num_examples: 720
  - name: test_sample4096
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    num_examples: 2373
  download_size: 2170014
  dataset_size: 56484726
configs:
- config_name: full_agreement
  data_files:
  - split: train
    path: full_agreement/train-*
  - split: val
    path: full_agreement/val-*
  - split: test
    path: full_agreement/test-*
  - split: heldout_item
    path: full_agreement/heldout_item-*
  - split: heldout_task
    path: full_agreement/heldout_task-*
  - split: val_sample1024
    path: full_agreement/val_sample1024-*
  - split: val_sample4096
    path: full_agreement/val_sample4096-*
  - split: test_sample1024
    path: full_agreement/test_sample1024-*
  - split: test_sample4096
    path: full_agreement/test_sample4096-*
---

# Multi-task Creativity Evaluation Dataset - Preference subset (MuCE-Pref)

This dataset is introduced in the [Creative Preference Optimization](https://arxiv.org/abs/2505.14442) and contains human responses and ratings for multiple creativity assessments.
It is derived from the [MuCE](https://huggingface.co/datasets/CNCL-Penn-State/MuCE) dataset and is in the [TRL Preference dataset format](https://huggingface.co/docs/trl/en/dataset_formats).

### Dataset Sources

See [Creative Preference Optimization](https://arxiv.org/abs/2505.14442) for a list of sources.

## Citation
```
@misc{ismayilzada2025creativepreferenceoptimization,
      title={Creative Preference Optimization}, 
      author={Mete Ismayilzada and Antonio Laverghetta Jr. and Simone A. Luchini and Reet Patel and Antoine Bosselut and Lonneke van der Plas and Roger E. Beaty},
      year={2025},
      eprint={2505.14442},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.14442}, 
}
```