Detecting and Correcting Hate Speech in Multimodal Memes with Large
  Visual Language Model
Minh-Hao Van Xintao Wu
Recently, large language models (LLMs) have taken the spotlight in natural
language processing. Further, integrating LLMs with vision enables the users to
explore more emergent abilities in multimodality. Visual language models
(VLMs), such as LLaVA, Flamingo, or GPT-4, have demonstrated impressive
performance on various visio-linguistic tasks. Consequently, there are enormous
applications of large models that could be potentially used on social media
platforms. Despite that, there is a lack of related work on detecting or
correcting hateful memes with VLMs. In this work, we study the ability of VLMs
on hateful meme detection and hateful meme correction tasks with zero-shot
prompting. From our empirical experiments, we show the effectiveness of the
pretrained LLaVA model and discuss its strengths and weaknesses in these tasks.