  Automatic hate speech detection using deep neural models is hampered by the
scarcity of labeled datasets, leading to poor generalization. To mitigate this
problem, generative AI has been utilized to generate large amounts of synthetic
hate speech sequences from available labeled examples, leveraging the generated
data in finetuning large pre-trained language models (LLMs). In this chapter,
we provide a review of relevant methods, experimental setups and evaluation of
this approach. In addition to general LLMs, such as BERT, RoBERTa and ALBERT,
we apply and evaluate the impact of train set augmentation with generated data
using LLMs that have been already adapted for hate detection, including
RoBERTa-Toxicity, HateBERT, HateXplain, ToxDect, and ToxiGen. An empirical
study corroborates our previous findings, showing that this approach improves
hate speech generalization, boosting recall performance across data
distributions. In addition, we explore and compare the performance of the
finetuned LLMs with zero-shot hate detection using a GPT-3.5 model. Our results
demonstrate that while better generalization is achieved using the GPT-3.5
model, it achieves mediocre recall and low precision on most datasets. It is an
open question whether the sensitivity of models such as GPT-3.5, and onward,
can be improved using similar techniques of text generation.
