Abstract
This paper describes our approach for SemEval-2024 Task 4: Multilingual Detection of Persuasion Techniques in Memes. Specifically, we concentrate on Subtask 2b, a binary classification challenge that entails categorizing memes as either “propagandistic” or “non-propagandistic”. To address this task, we utilized the large multimodal pretrained model, LLaVa. We explored various prompting strategies and fine-tuning methods, and observed that the model, when not fine-tuned but provided with a few-shot learning examples, achieved the best performance. Additionally, we enhanced the model’s multilingual capabilities by integrating a machine translation model. Our system secured the 2nd place in the Arabic language category.- Anthology ID:
- 2024.semeval-1.278
- Volume:
- Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2051–2056
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.278
- DOI:
- Cite (ACL):
- Charlie Grimshaw, Kalina Bontcheva, and Xingyi Song. 2024. SheffieldVeraAI at SemEval-2024 Task 4: Prompting and fine-tuning a Large Vision-Language Model for Binary Classification of Persuasion Techniques in Memes. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 2051–2056, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- SheffieldVeraAI at SemEval-2024 Task 4: Prompting and fine-tuning a Large Vision-Language Model for Binary Classification of Persuasion Techniques in Memes (Grimshaw et al., SemEval 2024)
- PDF:
- https://preview.aclanthology.org/ingestion-checklist/2024.semeval-1.278.pdf