xzuyn/beavertails-alpaca

---
size_categories:
- 100K<n<1M
---
# Original Dataset: [BeaverTails](https://huggingface.co/datasets/PKU-Alignment/BeaverTails)
```json
{
'Animal Abuse': {
True: 3480,
False: 297087
},
'Child Abuse': {
True: 1664,
False: 298903
},
'Controversial Topics, Politics': {
True: 9233,
False: 291334
},
'Discrimination, Stereotype, Injustice': {
True: 24006,
False: 276561
},
'Drug Abuse, Weapons, Banned Substance': {
True: 16724,
False: 283843
},
'Financial Crime, Property Crime, Theft': {
True: 28769,
False: 271798
},
'Hate Speech, Offensive Language': {
True: 27127,
False: 273440
},
'Misinformation Regarding Ethics, Laws And Safety': {
True: 3835,
False: 296732
},
'Non Violent Unethical Behavior': {
True: 59992,
False: 240575
},
'Privacy Violation': {
True: 14774,
False: 285793
},
'Self Harm': {
True: 2024,
False: 298543
},
'Sexually Explicit, Adult Content': {
True: 6876,
False: 293691
},
'Terrorism, Organized Crime': {
True: 2457,
False: 298110
},
'Violence, Aiding And Abetting, Incitement': {
True: 79544,
False: 221023
}
}
```
# Paper: [BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset](https://arxiv.org/abs/2307.04657)
```
@article{beavertails,
title = {BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset},
author = {Jiaming Ji and Mickel Liu and Juntao Dai and Xuehai Pan and Chi Zhang and Ce Bian and Chi Zhang and Ruiyang Sun and Yizhou Wang and Yaodong Yang},
journal = {arXiv preprint arXiv:2307.04657},
year = {2023}
}
```

原始信息汇总

数据集概述

数据集名称

BeaverTails

数据集大小

• 数据集大小范围：100K < n < 1M

数据集内容

数据集包含多个类别的数据，每个类别分为True和False两类，具体分布如下：

• Animal Abuse

  • True: 3480
  • False: 297087

• Child Abuse

  • True: 1664
  • False: 298903

• Controversial Topics, Politics

  • True: 9233
  • False: 291334

• Discrimination, Stereotype, Injustice

  • True: 24006
  • False: 276561

• Drug Abuse, Weapons, Banned Substance

  • True: 16724
  • False: 283843

• Financial Crime, Property Crime, Theft

  • True: 28769
  • False: 271798

• Hate Speech, Offensive Language

  • True: 27127
  • False: 273440

• Misinformation Regarding Ethics, Laws And Safety

  • True: 3835
  • False: 296732

• Non Violent Unethical Behavior

  • True: 59992
  • False: 240575

• Privacy Violation

  • True: 14774
  • False: 285793

• Self Harm

  • True: 2024
  • False: 298543

• Sexually Explicit, Adult Content

  • True: 6876
  • False: 293691

• Terrorism, Organized Crime

  • True: 2457
  • False: 298110

• Violence, Aiding And Abetting, Incitement

  • True: 79544
  • False: 221023