PPE-GPQA-Best-of-K

# Overview

This contains the GPQA correctness preference evaluation set for Preference Proxy Evaluations.

The prompts are sampled from [GPQA](https://huggingface.co/datasets/Idavidrein/gpqa).

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

[Paper](https://arxiv.org/abs/2410.14872)

[Code](https://github.com/lmarena/PPE)

# License

User prompts are licensed under CC BY 4.0, 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},
}
```

# 数据集概览
本数据集包含用于偏好代理评估（Preference Proxy Evaluations）的GPQA正确性偏好评估集。
本数据集的提示词（Prompt）采样自GPQA（https://huggingface.co/datasets/Idavidrein/gpqa）。
本数据集仅用于基准测试与模型评估，不得用于模型训练。
[论文](https://arxiv.org/abs/2410.14872)
[代码](https://github.com/lmarena/PPE)

# 授权协议
用户提示词遵循CC BY 4.0协议授权，模型输出则受对应模型提供商的使用条款约束。

# 引用格式

@misc{frick2024evaluaterewardmodelsrlhf,
title={如何为强化学习人类反馈（RLHF）评估奖励模型},
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},
}

搜集汇总

背景与挑战

背景概述

PPE-GPQA-Best-of-K是一个用于GPQA正确性偏好评估的数据集，专门设计用于基准测试和评估，而非训练目的。其许可证为Apache License 2.0，用户提示基于CC BY 4.0许可，模型输出则遵循相应模型提供商的条款。该数据集基于2024年的相关研究构建。