Arabic Train at NADI 2024 shared task: LLMs’ Ability to Translate Arabic Dialects into Modern Standard Arabic
Anastasiia Demidova, Hanin Atwany, Nour Rabih, Sanad Sha’ban · 2024
Navigating the intricacies of machine translation (MT) involves tackling the nuanced disparities between Arabic dialects and Modern Standard Arabic (MSA), presenting a formidable obstacle.In this study, we delve into Subtask 3 of the NADI shared task (Abdul-Mageed et al., 2024), focusing on the translation of sentences from four distinct Arabic dialects into MSA.Our investigation explores the efficacy of various models, including Jais, NLLB, GPT-3.5, and GPT-4, in this dialect-to-MSA translation endeavor.Our findings reveal that Jais surpasses all other models, boasting an average BLEU score of 19.48 in the combination of zero-and few-shot setting, whereas NLLB exhibits the least favorable performance, garnering a BLEU score of 8.77.