PKU-Alignment/BeaverTails

BeaverTails is a collection of AI‑safety‑focused datasets containing a series of human‑annotated question‑answer pairs, each labeled with a corresponding harm category. The dataset covers 14 harm categories such as animal abuse, child abuse, discrimination, hate speech, etc. It is intended for research, especially for creating safer, less‑harmful AI systems. The dataset includes multiple splits: 330k_train, 330k_test, 30k_train and 30k_test.

Updated 10/17/2023

hugging_face

Dataset Overview

Name: BeaverTails

Purpose: AI safety research aimed at developing safer, less‑harmful AI systems.

Content: Human‑labeled question‑answer (QA) pairs, each associated with one or more harm categories.

Harm Categories:

Animal abuse
Child abuse
Controversial topics, politics
Discrimination, stereotypes, unfairness
Drug abuse, weapons, prohibited substances
Financial crime, property crime, theft
Hate speech, offensive language
Misinformation about ethics, law, and safety
Non‑violent unethical behavior
Privacy violation
Self‑harm
Sexual content, adult content
Terrorism, organized crime
Violence, assistance and incitement, provocation

Dataset Size: 100K<n<1M

Data File Configuration:

Default configuration:
- Training set:
  - 330k_train: round0/330k/train.jsonl.xz
  - 30k_train: round0/30k/train.jsonl.gz
- Test set:
  - 330k_test: round0/30k/test.jsonl.xz
  - 30k_test: round0/30k/test.jsonl.gz

License: CC BY‑NC 4.0

Language: English

Task Category: Text classification

Tags: safety, AI safety, moderation, refusal sampling, LLM, LM, human feedback

Usage Warning: The dataset contains potentially offensive or disturbing content and should be used responsibly, respecting individual risk tolerance. It should not be used to train dialogue agents that might produce harmful behavior.

PKU-Alignment/BeaverTails

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