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.

翻译：在在线交流不断演变的背景下，审核仇恨言论（HS）构成了一项复杂挑战，而数字内容的多模态特性更使这一挑战雪上加霜。本综述深入探讨了HS审核领域的最新进展，聚焦于大型语言模型（LLMs）和大型多模态模型（LMMs）日益重要的作用。我们从对现有文献的全面分析入手，揭示了文本、视觉和听觉元素在传播HS时错综复杂的相互作用。我们发现，由于HS传播的复杂性和微妙性，整合这些模态已成为一个显著趋势。我们特别强调了LLMs和LMMs推动的进展，这些进展已开始重新定义检测与审核能力的边界。我们识别了研究中的现有空白，尤其是在代表性不足的语言和文化背景下，以及应对低资源场景方案的需求。本综述最后以前瞻性视角，概述了未来研究的潜在方向，包括探索新型人工智能方法、人工智能在审核中的伦理治理，以及开发更细致入微、具备上下文感知能力的系统。本全面概述旨在催化进一步研究，并促进各方协作，以在数字时代形成更精妙、更负责任且更以人为中心的HS审核方法。警告：本文包含冒犯性示例。