## 2 Motivation and Objectives  
In the fast-evolving field of NLP, the availability of high-quality, accessible data are critical. The present study was initiated with the primary goal of developing a freely accessible Arabic corpus, ArafFast, tailored to enhance NLP applications such as language modeling. The necessity for such a corpus stems from the considerable time and resources currently required for data collection, which poses a significant barrier to research and development in Arabic NLP.  
Arabic, being a rich and complex language, presents unique challenges in text processing and generation. Existing corpora are either lacking for comprehensive NLP tasks or are not freely available, or there is a need for extensive preprocessing, limiting their utility for widespread research and application development. By creating ArafFast, we aim to fill this gap, offering a robust Arabic corpus that supports not only basic NLP tasks, but also more advanced applications such as chatbots, question-answering (QA) systems, summarization tools, and sophisticated Arabic text generation.  
Establishing an Arabic corpus, particularly one drawn from broad and diverse content, holds significant importance for several reasons. Firstly, the collected corpora were assembled to create a ready-to-use corpus for future research, which helps in enriching NLP tasks such as the QA and text generation domains.  
Moreover, new language models, especially currently trending models such as large language models (LLMs), require rich information sources to become robust language models [9]. LLMs are built based on deep learning approaches. However, deep learning approaches are data-hungry, meaning that a large amount of data are required to improve the performance of such models [10].  
Furthermore, by combining corpora from various sources, biases present in individual corpora can be mitigated, leading to more balanced and fair models. Compiling multiple corpora can significantly expand the linguistic diversity covered. A larger and more diverse corpus can help in training models that generalized better to unseen data, thus improving their usability in real-world applications. A well-constructed corpus serves as a fundamental resource for linguistic studies and the development of NLP tasks.  
Another significant advantage of a ready-to-use corpus is the considerable time it saves for researchers. Rather than spending valuable time searching for, cleaning, and preprocessing data, researchers can use the corpus in its present condition and immediately begin the model training process. This process accelerates the research and development cycle, allowing researchers to focus more of their time on algorithms for building pretrained models and developing Arabic NLP applications.  
By using such a corpus, the findings of the present study are expected to contribute valuable resources that will aid in the advancement of Arabic NLP applications in the field of computational linguistics. In addition, the development of a large corpus will provide essential training and validation data that will help in building and training deep learning models, especially for tasks such as translation, sentiment analysis, and chatbot development.  
The objectives of the present study are threefold:  
1. To conduct a comprehensive literature review of existing freely available online MSA corpora.
2. To develop a large MSA corpus named AraFast.
3. To investigate the impact of using a large, segmented, clean Arabic corpus in NLP tasks, with a specific focus on QA.  
Through this work, AraFast is expected to become a key resource that will greatly improve the capabilities and efficiency of technologies working with the Arabic NLP.