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.\footnote{ \textcolor{red}{WARNING: This paper contains offensive examples.

翻译：在在线交流不断演变的背景下，仇恨言论（HS）的审核因其数字内容的多模态性而变得尤为复杂。本综述深入探讨了仇恨言论审核领域的最新进展，重点阐述了大语言模型（LLMs）和多模态大模型（LMMs）日益凸显的作用。我们首先对当前文献进行全面分析，揭示文本、视觉和音频元素在传播仇恨言论过程中的微妙互动。研究发现，由于仇恨言论传播的复杂性和隐蔽性，一个显著趋势是将这些模态进行整合。我们重点关注了LLMs和LMMs所推动的进展，这些模型已经开始重新定义检测与审核能力的边界。我们指出了当前研究中的空白，尤其是在代表性不足的语言和文化背景下，以及应对低资源环境所需的解决方案。本综述最后以前瞻性的视角，勾勒出未来研究的潜在方向，包括探索新型人工智能方法论、AI在审核中的伦理治理，以及开发更细致、更具情境感知的系统。本综述旨在激发进一步研究，并促进各方合作，以在数字时代实现更复杂、更负责任且更以人为本的仇恨言论审核方法。\footnote{\textcolor{red}{警告：本文包含冒犯性示例。}}