Abstract
The widespread presence of hate speech on the internet, including formats such as text-based tweets and multimodal 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.- Anthology ID:
- 2024.emnlp-main.445
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7785–7799
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.emnlp-main.445/
- DOI:
- 10.18653/v1/2024.emnlp-main.445
- Cite (ACL):
- Ming Shan Hee, Aditi Kumaresan, and Roy Ka-Wei Lee. 2024. Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 7785–7799, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning (Hee et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.emnlp-main.445.pdf