responsible-ai-labs/RAIL-HH-10K

RAIL-HH-10K 是一个多维度安全对齐数据集，包含 10,000 个高质量示例，覆盖 8 个伦理维度，其中 99.5% 的示例被标注。该数据集旨在促进 AI 安全和伦理研究，特别适合安全对齐研究、可解释 AI、基准测试和模型训练。数据集分为训练集、验证集和测试集，每个示例都包含详细的标注和评分，以及拒绝和选择的响应。RAIL-HH-10K 数据集在安全和用户影响方面取得了显著的改进，并提供了多维度安全评分工具，以帮助研究人员评估其数据。

RAIL-HH-10K is a large-scale safety dataset with 10,000 high-quality examples, providing near-complete coverage across eight ethical dimensions. It is designed to advance AI safety and ethics research, particularly suitable for safety alignment, interpretable AI, benchmarking, and model training. The dataset is split into training, validation, and test sets, with each example containing detailed annotations and scores, including both rejected and chosen responses. RAIL-HH-10K demonstrates significant improvements in safety and user impact, and offers a multi-dimensional safety scoring tool to assist researchers in evaluating their data.