Robustness of foundational models for hate speech detection: analyzing pretraining dynamics and finetuning settings

Abstract

Despite the widespread adoption, there is a lack of research into how various critical aspects of LLMs affect their performance in hate speech detection. Through five research questions, our findings and recommendations lay the groundwork for empirically investigating different aspects of LLMs’ use in hate speech detection. We deep dive into comparing different pretrained models, evaluating their seed robustness, finetuning settings, and the impact of pretraining data collection time. Our analysis reveals early peaks for downstream tasks during pretraining, the limited benefit of employing a more recent pretraining corpus, and the significance of specific layers during finetuning. We further call into question the use of domain-specific models and highlight the lack of dynamic datasets for benchmarking hate speech detection.