Beyond Accuracy: A Qualitative Analysis of Vision-Language Models for Hate Speech Detection in Memes
Memes have turned out to be a powerful tool through which individuals share their ideas concerning contemporary social and political problems. Their anonymity, as well as their ability to go viral, make them a powerful medium for spreading hate. It remains very difficult to identify such complex and context-dependent hate speech. Although they display excellent performance on multimodal tasks, vision-language models (VLMs) tend to ignore context, irony, and other subtle cues that play a key role in identifying hateful memes. In this work, we present a qualitative analysis of four state-of-the-art VLMs: LLaVA-7B, Qwen-VL, GPT-4o mini, and Claude 3 Haiku. We evaluate these models under zero...
| Authors | Muhammad Jawad Chowdhury, Adiba Hasan, Ishrak Hossain et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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