In the digital world, memes present a unique challenge for content moderation
due to their potential to spread harmful content. Although detection methods
have improved, proactive solutions such as intervention are still limited, with
current research focusing mostly on text-based content, neglecting the
widespread influence of multimodal content like memes. Addressing this gap, we
present \textit{MemeGuard}, a comprehensive framework leveraging Large Language
Models (LLMs) and Visual Language Models (VLMs) for meme intervention.
\textit{MemeGuard} harnesses a specially fine-tuned VLM, \textit{VLMeme}, for
meme interpretation, and a multimodal knowledge selection and ranking mechanism
(\textit{MKS}) for distilling relevant knowledge. This knowledge is then
employed by a general-purpose LLM to generate contextually appropriate
interventions. Another key contribution of this work is the
\textit{\textbf{I}ntervening} \textit{\textbf{C}yberbullying in
\textbf{M}ultimodal \textbf{M}emes (ICMM)} dataset, a high-quality, labeled
dataset featuring toxic memes and their corresponding human-annotated
interventions. We leverage \textit{ICMM} to test \textit{MemeGuard},
demonstrating its proficiency in generating relevant and effective responses to
toxic memes.

通过使用大语言模型（LLM）和视觉语言模型（VLM），本研究提出了一种全面框架 MemeGuard 用于干预恶意的 multimodal（多模态）网络迷因，测试结果表明 MemeGuard 在生成恰当和有效应对恶意迷因方面表现出色。