SemanticCuetSync at CheckThat! 2024: Finding Subjectivity in News Articles using Llama

This study introduces an LLM-based technique for detecting subjectivity and objectivity in English and Arabic news articles. Although several transformers, deep learning (DL), and machine learning (ML)- based techniques were exploited for the task, the LLM (Llama-3-8b) outperformed other models, obtaining the highest F1-scores of 72.6% (Arabic) and 50.36% (English). The suggested LLM-based solution provides a rank of 4th (Arabic) and 12th (English) in the task competition. The research emphasizes the potential of advanced LLMs like Llama-3-8b in achieving high subjectivity and objectivity detection accuracy, which is essential for applications in media analysis, sentiment analysis, and automated content moderation. This study contributes to developing robust multilingual text classification systems, paving the way for more sophisticated and accurate linguistic analysis tools.

Recommended citation: Paran, A. I., Hossain, M. S., Shohan, S. H., Hossain, J., Ahsan, S., & Hoque, M. M. (2024). SemanticCuetSync at CheckThat! 2024: Finding subjectivity in news article using Llama.
Download Paper