  In the evolving landscape of online communication, moderating hate speech
(HS) presents an intricate challenge, compounded by the multimodal nature of
digital content. This comprehensive survey delves into the recent strides in HS
moderation, spotlighting the burgeoning role of large language models (LLMs)
and large multimodal models (LMMs). Our exploration begins with a thorough
analysis of current literature, revealing the nuanced interplay between
textual, visual, and auditory elements in propagating HS. We uncover a notable
trend towards integrating these modalities, primarily due to the complexity and
subtlety with which HS is disseminated. A significant emphasis is placed on the
advances facilitated by LLMs and LMMs, which have begun to redefine the
boundaries of detection and moderation capabilities. We identify existing gaps
in research, particularly in the context of underrepresented languages and
cultures, and the need for solutions to handle low-resource settings. The
survey concludes with a forward-looking perspective, outlining potential
avenues for future research, including the exploration of novel AI
methodologies, the ethical governance of AI in moderation, and the development
of more nuanced, context-aware systems. This comprehensive overview aims to
catalyze further research and foster a collaborative effort towards more
sophisticated, responsible, and human-centric approaches to HS moderation in
the digital era. WARNING: This paper contains offensive examples.
