  As large language models become increasingly integrated into daily life,
detecting implicit toxicity across diverse contexts is crucial. To this end, we
introduce LifeTox, a dataset designed for identifying implicit toxicity within
a broad range of advice-seeking scenarios. Unlike existing safety datasets,
LifeTox comprises diverse contexts derived from personal experiences through
open-ended questions. Experiments demonstrate that RoBERTa fine-tuned on
LifeTox matches or surpasses the zero-shot performance of large language models
in toxicity classification tasks. These results underscore the efficacy of
LifeTox in addressing the complex challenges inherent in implicit toxicity. We
open-sourced the
dataset\footnote{\url{https://huggingface.co/datasets/mbkim/LifeTox}} and the
LifeTox moderator family; 350M, 7B, and 13B.
