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Recently, large language models (LLMs) have taken the spotlight in naturallanguage processing. Further, integrating LLMs with vision enables the users toexplore more emergent abilities in multimodality. Visual language models(VLMs), such as LLaVA, Flamingo, or GPT-4, have demonstrated impressiveperformance on various visio-linguistic tasks. Consequently, there are enormousapplications of large models that could be potentially used on social mediaplatforms. Despite that, there is a lack of related work on detecting orcorrecting hateful memes with VLMs. In this work, we study the ability of VLMson hateful meme detection and hateful meme correction tasks with zero-shotprompting. From our empirical experiments, we show the effectiveness of thepretrained LLaVA model and discuss its strengths and weaknesses in these tasks.

Detecting and Correcting Hate Speech in Multimodal Memes with Large Visual Language Model