SubmissionNumber#=%=#302
FinalPaperTitle#=%=#SheffieldVeraAI at SemEval-2024 Task 4: Prompting and fine-tuning a Large Vision-Language Model for Binary Classification of Persuasion Techniques in Memes
ShortPaperTitle#=%=#
NumberOfPages#=%=#6
CopyrightSigned#=%=#Charlie Grimshaw
JobTitle#==#PhD Student
Organization#==#
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.
Author{1}{Firstname}#=%=#Charlie Radley
Author{1}{Lastname}#=%=#Grimshaw
Author{1}{Username}#=%=#charliegrimshaw
Author{1}{Email}#=%=#cgrimshaw1@sheffield.ac.uk
Author{1}{Affiliation}#=%=#University of Sheffield
Author{2}{Firstname}#=%=#Kalina
Author{2}{Lastname}#=%=#Bontcheva
Author{2}{Username}#=%=#kbontcheva
Author{2}{Email}#=%=#k.bontcheva@dcs.shef.ac.uk
Author{2}{Affiliation}#=%=#University of Sheffield
Author{3}{Firstname}#=%=#Xingyi
Author{3}{Lastname}#=%=#Song
Author{3}{Username}#=%=#deansong
Author{3}{Email}#=%=#x.song@sheffield.ac.uk
Author{3}{Affiliation}#=%=#University of Sheffield

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