  Academic researchers and social media entities grappling with the
identification of hate speech face significant challenges, primarily due to the
vast scale of data and the dynamic nature of hate speech. Given the ethical and
practical limitations of large predictive models like ChatGPT in directly
addressing such sensitive issues, our research has explored alternative
advanced transformer-based and generative AI technologies since 2019.
Specifically, we developed a new data labeling technique and established a
proof of concept targeting anti-Semitic hate speech, utilizing a variety of
transformer models such as BERT (arXiv:1810.04805), DistillBERT
(arXiv:1910.01108), RoBERTa (arXiv:1907.11692), and LLaMA-2 (arXiv:2307.09288),
complemented by the LoRA fine-tuning approach (arXiv:2106.09685). This paper
delineates and evaluates the comparative efficacy of these cutting-edge methods
in tackling the intricacies of hate speech detection, highlighting the need for
responsible and carefully managed AI applications within sensitive contexts.
