A Comprehensive Framework and Empirical Analysis for Evaluating Large Language Models in Arabic Dialect Identification

The widespread interest in large language models (LLMs) is rooted in their remarkable capacity to generate human-like and contextually relevant responses. However, the precision of LLMs within specific domains or intricate tasks, such as Arabic dialect identification, remains largely unexplored. This task presents a substantial challenge in Arabic natural language processing, given its language-dependent nature. This paper provides a framework for evaluating LLMs for Arabic dialect identification and conducts a comprehensive evaluation of LLMs, employing both tuning-free and fine-tuning-based learning paradigms. The evaluation encompasses GPT-3.5, chatGPT, GPT-4, and Google BARD for Arabic dialect identification under the tuning-free learning paradigm. Furthermore, it assesses the performance of GPT-3.5 along with AraBERT and MARBERT using the fine-tuning learning paradigm. In the tuning-free approach, GPT-4 achieves the most favorable results, reporting an F1MAC of 45.60%. Under the fine-tuning learning paradigm, both AraBERT and MARBERT exhibit comparable performance (around 50% F 1MAC) to GPT-3.5, without incurring any financial costs, in contrast to the expenses associated with GPT-3.5.