The translation between Modern Standard Arabic (MSA) and the various Arabic dialects presents unique challenges due to the significant linguistic, cultural, and contextual variations across the regions where Arabic is spoken. This paper presents a system description of our participation in the OSACT 2024 Dialect to MSA Translation Shared Task. We explain our comprehensive approach which combines data augmentation techniques using generative pre-trained transformer models (GPT-3.5 and GPT-4) with fine-tuning of AraT5 V2, a model specifically designed for Arabic translation tasks. Our methodology has significantly expanded the training dataset, thus improving the model’s performance across five major Arabic dialects, namely Gulf, Egyptian, Levantine, Iraqi, and Maghrebi. We have rigorously evaluated our approach, using BLEU score, to ensure translation accuracy, fluency, and the preservation of meaning. Our results showcase the effectiveness of our refined models in addressing the challenges posed by diverse Arabic dialects and Modern Standard Arabic (MSA), achieving a BLEU score of 80% on the validation test set and 22.25% on the blind test set. However, it’s important to note that while utilizing a larger dataset, such as Madar + Dev, resulted in significantly higher evaluation BLEU scores, the performance on the blind test set was relatively lower. This observation underscores the importance of dataset size in model training, revealing potential limitations in generalization to unseen data due to variations in data distribution and domain mismatches.

This research delves into the issue of hallucination detection in Large Language Models (LLMs) using Arabic language datasets. As LLMs are increasingly being used in various applications, the phenomenon of hallucination, which refers to generating factually inaccurate content despite grammatical coherence, poses significant challenges. We participate in the OSACT 2024 Shared-task (Detection of Hallucination in Arabic Factual Claims Generated by ChatGPT and GPT4). We explore various approaches for detecting and mitigating hallucination, using models such as GPT-4, Mistral, and Gemini within a novel experimental framework. Our research findings reveal that the effectiveness of these models in classifying claims into Fact-Claim, Fact-Improvement, and Non-Fact categories varies greatly, underscoring the complexities of addressing hallucination in morphologically rich languages. The study emphasizes the need for advanced modelling and training strategies to enhance the reliability and factual accuracy of LLM-generated content, laying the groundwork for future explorations in mitigating hallucination risks. In our experiments we achieved a 0.54 F1 in GPT-4 LLM.