@inproceedings{yamagishi-2024-yyama,
title = "{YY}ama@Multimodal Hate Speech Event Detection 2024: Simpler Prompts, Better Results - Enhancing Zero-shot Detection with a Large Multimodal Model",
author = "Yamagishi, Yosuke",
editor = {H{\"u}rriyeto{\u{g}}lu, Ali and
Tanev, Hristo and
Thapa, Surendrabikram and
Uludo{\u{g}}an, G{\"o}k{\c{c}}e},
booktitle = "Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.case-1.7",
pages = "60--66",
abstract = "This paper introduces a zero-shot hate detection experiment using a multimodal large model. Although the implemented model comprises an unsupervised method, results demonstrate that its performance is comparable to previous supervised methods. Furthemore, this study proposed experiments with various prompts and demonstrated that simpler prompts, as opposed to the commonly used detailed prompts in large language models, led to better performance for multimodal hate speech event detection tasks. While supervised methods offer high performance, they require significant computational resources for training, and the approach proposed here can mitigate this issue.The code is publicly available at https://github.com/yamagishi0824/zeroshot-hate-detect.",
}
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%0 Conference Proceedings
%T YYama@Multimodal Hate Speech Event Detection 2024: Simpler Prompts, Better Results - Enhancing Zero-shot Detection with a Large Multimodal Model
%A Yamagishi, Yosuke
%Y Hürriyetoğlu, Ali
%Y Tanev, Hristo
%Y Thapa, Surendrabikram
%Y Uludoğan, Gökçe
%S Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F yamagishi-2024-yyama
%X This paper introduces a zero-shot hate detection experiment using a multimodal large model. Although the implemented model comprises an unsupervised method, results demonstrate that its performance is comparable to previous supervised methods. Furthemore, this study proposed experiments with various prompts and demonstrated that simpler prompts, as opposed to the commonly used detailed prompts in large language models, led to better performance for multimodal hate speech event detection tasks. While supervised methods offer high performance, they require significant computational resources for training, and the approach proposed here can mitigate this issue.The code is publicly available at https://github.com/yamagishi0824/zeroshot-hate-detect.
%U https://aclanthology.org/2024.case-1.7
%P 60-66
Markdown (Informal)
[YYama@Multimodal Hate Speech Event Detection 2024: Simpler Prompts, Better Results - Enhancing Zero-shot Detection with a Large Multimodal Model](https://aclanthology.org/2024.case-1.7) (Yamagishi, CASE-WS 2024)
ACL