The widespread presence of hate speech on the internet, including formats such as text-based tweets and vision-language memes, poses a significant challenge to digital platform safety. Recent research has developed detection models tailored to specific modalities; however, there is a notable gap in transferring detection capabilities across different formats. This study conducts extensive experiments using few-shot in-context learning with large language models to explore the transferability of hate speech detection between modalities. Our findings demonstrate that text-based hate speech examples can significantly enhance the classification accuracy of vision-language hate speech. Moreover, text-based demonstrations outperform vision-language demonstrations in few-shot learning settings. These results highlight the effectiveness of cross-modality knowledge transfer and offer valuable insights for improving hate speech detection systems.
@article{arxiv.2410.05600,
title = {Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning},
author = {Ming Shan Hee and Aditi Kumaresan and Roy Ka-Wei Lee},
journal= {arXiv preprint arXiv:2410.05600},
year = {2024}
}