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：在不断演变的在线交流环境中，仇恨言论（HS）的审核呈现出一项复杂的挑战，而数字内容的多模态特性更使其雪上加霜。本综述深入探讨了HS审核领域的最新进展，重点关注大型语言模型（LLMs）和大型多模态模型（LMMs）日益重要的作用。我们的探索始于对现有文献的全面分析，揭示了文本、视觉和听觉元素在传播HS过程中微妙的相互作用。我们发现一个明显的趋势是整合这些模态，这主要是由于HS传播方式的复杂性和隐蔽性。综述重点强调了LLMs和LMMs所推动的进展，这些模型已开始重新定义检测与审核能力的边界。我们指出了现有研究中的空白，特别是在代表性不足的语言和文化语境下，以及处理低资源场景解决方案的迫切需求。综述以展望未来的视角作结，概述了潜在的研究方向，包括探索新颖的人工智能方法、审核中人工智能的伦理治理，以及开发更细致、更具上下文感知能力的系统。本综述旨在促进进一步研究，并推动协作努力，以在数字时代发展出更复杂、更负责任、更以人为本的HS审核方法。警告：本文包含冒犯性示例。