Large Language Models for Multi-label Propaganda Detection

• URL: http://arxiv.org/abs/2210.08209v1
• Date: Sat, 15 Oct 2022 06:47:31 GMT
• Title: Large Language Models for Multi-label Propaganda Detection
• Authors: Tanmay Chavan and Aditya Kane
• Abstract summary: We describe our approach for the WANLP 2022 shared task which handles the task of propaganda detection in a multi-label setting. The task demands the model to label the given text as having one or more types of propaganda techniques. We show that an ensemble of five models performs the best on the task, scoring a micro-F1 score of 59.73%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The spread of propaganda through the internet has increased drastically over the past years. Lately, propaganda detection has started gaining importance because of the negative impact it has on society. In this work, we describe our approach for the WANLP 2022 shared task which handles the task of propaganda detection in a multi-label setting. The task demands the model to label the given text as having one or more types of propaganda techniques. There are a total of 22 propaganda techniques to be detected. We show that an ensemble of five models performs the best on the task, scoring a micro-F1 score of 59.73%. We also conduct comprehensive ablations and propose various future directions for this work.

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