Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning

• URL: http://arxiv.org/abs/2410.05600v1
• Date: Tue, 8 Oct 2024 01:27:12 GMT
• Title: Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning
• Authors: Ming Shan Hee, Aditi Kumaresan, Roy Ka-Wei Lee,
• Abstract summary: Hate speech on the internet poses a significant challenge to digital platform safety. Recent research has developed detection models tailored to specific modalities. This study conducts extensive experiments using few-shot in-context learning with large language models.
• Score: 4.136573141724715
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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