Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning (2024.emnlp-main)
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| Challenge: | Recent research has developed models targeting specific modalities but lacks transferability between formats. |
| Approach: | They conduct extensive experiments using few-shot in-context learning with large language models to explore the transferability of hate speech detection between modalities. |
| Outcome: | The proposed model outperforms vision-language demonstrations in few-shot learning settings. |
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| Challenge: | a lack of labeled, non-English resources for hate speech detection limits research on hate speech . a recent study shows that zero-shot, cross-lingual learning models cannot be used as they are . lack of consistency limits research, and lack of models for non-english languages limits learning . |
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| Challenge: | Zero-shot cross-lingual transfer learning has been shown to be challenging for tasks involving a lot of linguistic specificities or when a cultural gap is present between languages, such as hate speech detection. |
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| Challenge: | Recent efforts to detect hateful or toxic language using large language models have not used explanation, additional context and victim community information in the detection process. |
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| Challenge: | Recent studies suggest that classifying hateful posts in a binary manner may not address nuanced task of detecting implicit hate speech. |
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| Challenge: | Existing methods for detecting hate speech ignore misalignment and uncertainty between modalities . social media platforms have become conduits for the rapid dissemination of hate speech . |
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Data-Efficient Methods For Improving Hate Speech Detection (2023.findings-eacl)
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| Challenge: | Existing methods for hate speech detection are data-hungry and require large datasets. |
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