lmarena-ai/PPE-MBPP-Plus-Best-of-K

Dataset

1

stars

4

commits

1

linked in READMEs

Oct 22, 2024

updated

README

Overview

This contains the MBPP-Plus correctness preference evaluation set for Preference Proxy Evaluations.

The prompts are sampled from MBPP-Plus.

This dataset is meant for benchmarking and evaluation, not for training.

Paper

Code

License

User prompts are licensed under Apache-2.0, and model outputs are governed by the terms of use set by the respective model providers.

Citation

@misc{frick2024evaluaterewardmodelsrlhf,
      title={How to Evaluate Reward Models for RLHF}, 
      author={Evan Frick and Tianle Li and Connor Chen and Wei-Lin Chiang and Anastasios N. Angelopoulos and Jiantao Jiao and Banghua Zhu and Joseph E. Gonzalez and Ion Stoica},
      year={2024},
      eprint={2410.14872},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2410.14872}, 
}

Contributors

evanfrick

2 commits

Timmli

2 commits

lmarena-ai/PPE-MBPP-Plus-Best-of-K

Dataset

1

stars

4

commits

1

linked in READMEs

Oct 22, 2024

updated

README

Overview

This contains the MBPP-Plus correctness preference evaluation set for Preference Proxy Evaluations.

The prompts are sampled from MBPP-Plus.

This dataset is meant for benchmarking and evaluation, not for training.

Paper

Code

License

User prompts are licensed under Apache-2.0, and model outputs are governed by the terms of use set by the respective model providers.

Citation

@misc{frick2024evaluaterewardmodelsrlhf,
      title={How to Evaluate Reward Models for RLHF}, 
      author={Evan Frick and Tianle Li and Connor Chen and Wei-Lin Chiang and Anastasios N. Angelopoulos and Jiantao Jiao and Banghua Zhu and Joseph E. Gonzalez and Ion Stoica},
      year={2024},
      eprint={2410.14872},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2410.14872}, 
}

Linked in READMEs

Contributors

evanfrick

2 commits

Timmli

2 commits