Beyond Accuracy: A Qualitative Analysis of Vision-Language Models for Hate Speech Detection in Memes

Abstract: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-shot and few-shot prompting to examine how contextual framing influences their outputs. Our analysis goes beyond simple classification accuracy and focuses on a qualitative evaluation of the models' generated justifications, providing a more in-depth understanding of their thought processes and constraints when dealing with hateful memes.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.26143 [cs.CL]

(or arXiv:2608.26143v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.26143 Focus to learn more arXiv-issued DOI via DataCite

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