## 6 Comparison of the AraFastQA Model Versions and the ARBERT Model  
### AraFastQA-Mini and AraFastQA-Base  
In the following section, the fourth research question (RQ4), \"How does pretraining a transformer model on a large Arabic corpus impact its accuracy in NLP tasks, specifically Arabic QA tasks?\", is answered. To explore the above question, a case study using the AraFast corpus and examining the effects of employing large and small Arabic corpora is presented. The significance of utilizing extensive Arabic corpora for training transformer models has consistently been emphasized in prior research. The authors of several papers have highlighted the effectiveness of large-scale Arabic language models and their applications across various NLP tasks. For instance, [PERSON] and [PERSON] [71] discussed, in their study, the development of a large Arabic language model using Funnel Transformer architecture combined with the ELECTRA objective. Their work underscores the importance of using a substantial corpus to train models to efficiently handle the intricacies of the Arabic language. In another study, [PERSON] et al. [72] introduced AraGPT2, a pre-trained transformer model for Arabic language generation. The researchers emphasized the necessity of a large diverse Arabic corpus for pretraining to enhance the model's performance in generation tasks. Moreover, [PERSON] et al. [73] introduced ARBERT and MARBERT in their study, deep bidirectional transformers for Arabic, and highlighted how large-scale Arabic corpora enable these models to better capture the nuances of the language, leading to improvements in various downstream tasks. Furthermore, [PERSON] and [PERSON] [74] focused on using deep transformer language models for Arabic text summarization. Their study underscores the importance of using extensive corpora in training models to effectively summarize Arabic texts, highlighting the vital role of data volume in model efficacy. In another study, [PERSON] et al. [75] explored the interpretation ability of Arabic transformer models. They discussed the impacts of large-scale Arabic data on the interpretability and effectiveness of transformer models when processing data in the Arabic language. To demonstrate the impact of using large and small Arabic corpora, we examined the two proposed Arabic transformer models: AraFastQA-mini, and AraFastQA-base. Moreover, a few-shot learning (FSL) approach was adopted to assess the proposed models' performance in an Arabic QA task. FSL focuses on enabling models to perform language tasks with minimal training data, a crucial consideration in NLP, since the process of obtaining large, annotated datasets can be expensive and time consuming. For instance, by exploring low-shot learning in NLP, [PERSON] et al. [76] addressed, in their study, the challenges of training models with a limited amount of data. Similarly, [PERSON] and [PERSON] [77] discussed, using bidirectional prompt learning in NLP, a few-shot tasks in their study, highlighting the models' adaptability to effective learning from a limited number of examples. [PERSON] et al. [78] investigated continual few-shot learning for text classification, and highlighted FSL's potential in dynamically evolving NLP applications. The findings of the above studies underscore the growing interest in techniques that reduce dependency on large datasets, while facilitating effective performance in various NLP tasks. In Table 6, the results of tuning the two proposed models using the FSL approach are presented. The F1 and exact match (EM) scores represent the models' evaluation results in the Arabic Typologically Diverse Question Answering (TyDi QA) dataset, a benchmark for information-seeking question answering in topologically diverse languages, including Arabic [79]. In Table 6, Figures 12 and 13, the performance comparison results of the AraFastQA-mini and AraFastQA-base models in the Arabic QA task with the Arabic TyDi QA dataset are presented. The evaluation metrics F1 score and EM are standard metrics for assessing QA systems.