  The spread of toxic content online is an important problem that has adverse
effects on user experience online and in our society at large. Motivated by the
importance and impact of the problem, research focuses on developing solutions
to detect toxic content, usually leveraging machine learning (ML) models
trained on human-annotated datasets. While these efforts are important, these
models usually do not generalize well and they can not cope with new trends
(e.g., the emergence of new toxic terms). Currently, we are witnessing a shift
in the approach to tackling societal issues online, particularly leveraging
large language models (LLMs) like GPT-3 or T5 that are trained on vast corpora
and have strong generalizability. In this work, we investigate how we can use
LLMs and prompt learning to tackle the problem of toxic content, particularly
focusing on three tasks; 1) Toxicity Classification, 2) Toxic Span Detection,
and 3) Detoxification. We perform an extensive evaluation over five model
architectures and eight datasets demonstrating that LLMs with prompt learning
can achieve similar or even better performance compared to models trained on
these specific tasks. We find that prompt learning achieves around 10\%
improvement in the toxicity classification task compared to the baselines,
while for the toxic span detection task we find better performance to the best
baseline (0.643 vs. 0.640 in terms of $F_1$-score). Finally, for the
detoxification task, we find that prompt learning can successfully reduce the
average toxicity score (from 0.775 to 0.213) while preserving semantic meaning.
