Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning
<|reference_start|>Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning: 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.<|reference_end|>
@article{hee2024bridging,
    title={Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with
  Few-Shot In-Context Learning},
    author={Ming Shan Hee, Aditi Kumaresan, Roy Ka-Wei Lee},
    journal={arXiv preprint arXiv:2410.05600},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05600},
    primaryClass={cs.CL}
}