korean safety benchmarks
1.0.0
该仓库提供了以下两篇论文的代码和数据集：
我们的SQuARe数据集可以在data/SQuARe/中找到。有关数据集的详细信息，请参阅SQuARe论文。
我们还在data/SQuARe/with_raw_annotations中发布了带有原始注释的数据集。由于我们数据集中的问题和回答本质上是主观的，我们相信原始注释将有助于进一步研究注释者之间的分歧。
注意：虽然我们的数据集包含英文翻译，但直接使用它时需要小心，因为我们使用的敏感主题反映了韩国社会的特质。我们建议研究人员建立自己的数据集。
数据集生成的管道可以在pipeline/square中找到。
我们的KoSBi数据集可以在data/KosBi/中找到。有关数据集的详细信息，请参阅KoSBi论文。
更新：我们通过运行额外的迭代收集了更多数据。您可以在名为data/KoSBi/kosbi_v2_{train,valid,test}.json的文件中找到它们，其中包含原始 KoSBi 数据集。 （上下文，句子）对的总数已增加到近 68k，其中 34.2k 安全句子和 33.8k 不安全句子。
与SQuARe类似，数据集生成的管道可以在pipeline/kosbi中找到。
Korean-Safety-Benchmarks
MIT License
Copyright 2023-present NAVER Cloud Corp.
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@inproceedings{lee2023square,
title={SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human-Machine Collaboration},
author={Hwaran Lee and Seokhee Hong and Joonsuk Park and Takyoung Kim and Meeyoung Cha and Yejin Choi and Byoung Pil Kim and Gunhee Kim and Eun-Ju Lee and Yong Lim and Alice Oh and Sangchul Park and Jung-Woo Ha},
booktitle={Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics},
year={2023}
}
@inproceedings{lee2023kosbi,
title={KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application},
author={Hwaran Lee and Seokhee Hong and Joonsuk Park and Takyoung Kim and Gunhee Kim and Jung-Woo Ha},
booktitle={Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics: Industry Track},
year={2023}
}
如果您对我们的数据集或代码有任何疑问，请随时询问我们：Seokhee Hong ([email protected]) 或 Hwaran Lee ([email protected])
