Bibliographic Details
| Title: |
Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning |
| Authors: |
Hee, Ming Shan, Kumaresan, Aditi, Lee, Roy Ka-Wei |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computation and Language |
| More Details: |
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.
Comment: Accepted at EMNLP'24 (Main) |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2410.05600 |
| Accession Number: |
edsarx.2410.05600 |
| Database: |
arXiv |
