L omonosov MSU at S em E val-2024 Task 4: Comparing LLM s and embedder models to identifying propaganda techniques in the content of memes in E nglish for subtasks No1, No2a, and No2b (2024)

Abstract: This paper presents the solution of the LomonosovMSU team for the SemEval-2024 Task 4 “Multilingual Detection of Persuasion Techniques in Memes” competition for the English language task. During the task solving process, generative and BERT-like (training classifiers on top of embedder models) approaches were tested for subtask No1, as well as an BERT-like approach on top of multimodal embedder models for subtasks No2a/No2b. The models were trained using datasets provided by the competition organizers, enriched with filtered datasets from previous SemEval competitions. The following results were achieved: 18th place for subtask No1, 9th place for subtask No2a, and 11th place for subtask No2b.

## introduction in the modern world, memes are one of the most popular forms of delivering information to social media users. unfortunately, memes created using a variety of rhetorical and psychological techniques are also used to conduct disinformation campaigns. the overall goal of the semeval-2024 task 4 competition is to build models to detect rhetorical and psychological propaganda techniques in memes. the competition itself consists of three subtasks: in this work, experiments were conducted with generative and bert-like models to solve subtask №1 and classifiers on top of multimodal models to solve subtasks №2a/№2b. bert-like approaches refer to the creation classifiers over embedder models and will be used further in this article. all tasks were solved for datasets in english. the following results were achieved: 18th place for subtask №1, 9th place for subtask №2a, and 11th place for subtask №2b. the code for the solutions is available in the repository at github foot_1 . ## related works supervised fine-tuning (sft) @xcite is one of the most popular methods for fine-tuning llms to solve various tasks. in this work, we conducted fine-tuning of llms, such as llama foot_2 and mistral @xcite , for detecting propaganda techniques. unfortunately, sft is very resource-intensive, and conducting frequent experiments with fine-tuning llms is time-consuming and expensive. to address this problem, a less resource-intensive approach, lora @xcite , was used, but unfortunately, the results of this approach were not satisfactory. in parallel with sft and lora approaches, experiments were conducted on fine-tuning simple classifier layers on top of embedding models: debert @xcite , clip @xcite , blip @xcite . the results of these experiments yielded comparable results to experiments with llms. ## further research we did not have time to take more powerful models and experiment with them to solve subtask №1, but we are confident that mixtral 8x7b or llama-2-13b-chat would yield better results. additionally, we did not have time to add data from the ptc @xcite corpus to the dataset, but we are sure that it would have provided an even greater improvement. we also did not have time to test generative models that take text with images as input and generate text responses, for example, rudolph @xcite .