# Exploring GPT-4 Capabilities in Generating Paraphrased Sentences for the Arabic Language
## 1 Introduction  
NLP focuses on the interaction between computers and human (natural) languages. It is a field considered to be at the intersection of computer science, Artificial Intelligence (AI), and computational linguistics [1]. AI is defined as \"the automation of activities that we associate with human thinking, activities such as decision-making, problem-solving, and learning\" [2]. NLP has numerous applications based on the textual form, for instance, a chatbot (chatter robot) [3], autocomplete suggestions [4], MT, QA [5], and paraphrase generation [6]. Paraphrasing transforms a given text into another text while keeping its semantic meaning [7]. Paraphrase generation significantly impacts several NLP applications because it allows the use of different word expressions to create similar text. Therefore, generating an alternative text for the same meaning enhances the performance of other NLP applications such as semantic parsing, machine translation, query formulation in web searching, question answering, and data augmentation [6, 8]. ML is a branch of AI that investigatescomputer learnability based on data [9]. Recent advancements in ML have significantly impacted NLP [10], and there are many ML approaches to generating paraphrasing.  
ML approaches include rule-based [11], sequence-to-sequence [12], reinforcement learning [6], transformer model [12], and generative models [13]. GPT is a generative model commonly used for text-generation purposes [14]. However, GPT could be used for purposes exceeding text generation. For instance, [PERSON] et al. (2023) [10] use Arabic GPT-2 (AraGPT-2) for data augmentation, [PERSON] et al. (2022) [15] utilize GPT-3 to improve Knowledge-based Visual Question Answering (VQA), and [PERSON] et al. (2023) [16] investigate the performance of ChatGPT-3.5 and ChatGPT-4.0 in proofreading and rephrasing a given paragraph in English. To our knowledge, GPT-4's capability in Arabic paraphrase generation remains underexplored, although Arabic is one of the most widely spoken languages globally. This gap shows a significant research opportunity to evaluate GPT-4 capabilities in producing high-quality, lexically diverse, and semantically equivalent paraphrased sentences for Arabic text, which can be used across various downstream applications. Additionally, our proposed methodologies and approaches can be adapted to languages with similar morphological and syntactic structures.  
This research explores and evaluates GPT's capabilities in paraphrasing Arabic text. To optimize paraphrasing performance, this study identifies effective prompt engineering and inquiries about similarity scores for paraphrased and non-paraphrased pairs. In addition, paraphrase generation is covered for text generally in different domains but without studying the impact of domains [17]; thus, this study investigates the impact of text categories on the quality of generated paraphrased sentences. This research proposes a detailed evaluation framework for assessing paraphrasing quality, comprising lexical diversity, content retention, and semantic similarity. Furthermore, this work includes releasing a new Arabic paraphrasing corpus containing approximately 200 K pairs, which is designed to support future research and advancements in AI and Arabic NLP research. For instance, the corpus could be used to build a new paraphrasing model for text in specific domains, and the corpus might combine with datasets from other languages to generate a multilingual paraphrasing model. On the other hand, the corpus could be used to analyze AI-generated text from linguistic perspectives, such as investigating word selection and sentence structure. This paper is organized as follows. Section 2 reviews related work and fundamental concepts, while Section 3 outlines the research methodology. Section 4 then presents the results and discussion, focusing on the expected corpus statistics and their evaluation.