  In this paper, we introduce the BeaverTails dataset, aimed at fostering
research on safety alignment in large language models (LLMs). This dataset
uniquely separates annotations of helpfulness and harmlessness for
question-answering pairs, thus offering distinct perspectives on these crucial
attributes. In total, we have gathered safety meta-labels for 333,963
question-answer (QA) pairs and 361,903 pairs of expert comparison data for both
the helpfulness and harmlessness metrics. We further showcase applications of
BeaverTails in content moderation and reinforcement learning with human
feedback (RLHF), emphasizing its potential for practical safety measures in
LLMs. We believe this dataset provides vital resources for the community,
contributing towards the safe development and deployment of LLMs. Our project
page is available at the following URL:
https://sites.google.com/view/pku-beavertails.
