Large language models (LLMs) have demonstrated impressive performance on
various downstream tasks without requiring fine-tuning, including ChatGPT, a
chat-based model built on top of LLMs such as GPT-3.5 and GPT-4. Despite having
a lower training proportion compared to English, these models also exhibit
remarkable capabilities in other languages. In this study, we assess the
performance of GPT-3.5 and GPT-4 models on seven distinct Arabic NLP tasks:
sentiment analysis, translation, transliteration, paraphrasing, part of speech
tagging, summarization, and diacritization. Our findings reveal that GPT-4
outperforms GPT-3.5 on five out of the seven tasks. Furthermore, we conduct an
extensive analysis of the sentiment analysis task, providing insights into how
LLMs achieve exceptional results on a challenging dialectal dataset.
Additionally, we introduce a new Python interface
this https URL that facilitates the evaluation of these tasks
effortlessly.

本研究评估了 GPT-3.5 和 GPT-4 模型在七个阿拉伯语自然语言处理任务上的性能，并发现 GPT-4 在其中五个任务上的性能优于 GPT-3.5，同时提供了一种新的 Python 接口用于轻松评估这些任务。