Detecting and Correcting Hate Speech in Multimodal Memes with Large Visual Language Model
This addresses the issue of hate speech on social media platforms, but it is incremental as it applies existing VLMs to a new application area.
The paper tackled the problem of detecting and correcting hate speech in multimodal memes using large visual language models (VLMs) like LLaVA with zero-shot prompting, showing their effectiveness in these tasks.
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.