SMASH at StanceEval 2024: Prompt Engineering LLMs for Arabic Stance Detection
Youssef Al Hariri, Ibrahim Abu Farha · 2024
This paper presents our submission for the Stance Detection in Arabic Language (StanceEval) 2024 shared task conducted by Team SMASH of the University of Edinburgh.We evaluated the performance of various BERTbased and large language models (LLMs).MARBERT demonstrates superior performance among the BERT-based models, achieving F1 and macro-F1 scores of 0.570 and 0.770, respectively.In contrast, the Command-R model outperforms all models with the highest overall F1 score of 0.661 and macro F1 score of 0.820.