Prompting for Multimodal Hateful Meme Classification
This addresses the problem of detecting hate speech in memes for social media moderation, but it is incremental as it adapts existing prompt-based methods to a specific multimodal task.
The paper tackled hateful meme classification by proposing PromptHate, a prompt-based model that uses pre-trained language models with simple prompts and in-context examples, achieving a high AUC of 90.96 and outperforming state-of-the-art baselines.
Hateful meme classification is a challenging multimodal task that requires complex reasoning and contextual background knowledge. Ideally, we could leverage an explicit external knowledge base to supplement contextual and cultural information in hateful memes. However, there is no known explicit external knowledge base that could provide such hate speech contextual information. To address this gap, we propose PromptHate, a simple yet effective prompt-based model that prompts pre-trained language models (PLMs) for hateful meme classification. Specifically, we construct simple prompts and provide a few in-context examples to exploit the implicit knowledge in the pre-trained RoBERTa language model for hateful meme classification. We conduct extensive experiments on two publicly available hateful and offensive meme datasets. Our experimental results show that PromptHate is able to achieve a high AUC of 90.96, outperforming state-of-the-art baselines on the hateful meme classification task. We also perform fine-grained analyses and case studies on various prompt settings and demonstrate the effectiveness of the prompts on hateful meme classification.