Synthetic Data Generation and Fine-Tuning for Saudi Arabic Dialect Adaptation

Abstract

Despite rapid developments and achievements in natural language processing, Saudi-altered dialects remain traditionally heavily underrepresented in mainstream models due to data silence, phonological variations, and geographic idiosyncrasies. To combat these problems, latest research has suggested the joint use of synthetic data production and fine-tuning strategies for dialect adaptation. The present study synthesizes knowledge from 30 peer-reviewed and preprint articles to assess state-of-the-art approaches in generating artificial data and fine-tuning LLMs for Saudi dialects.

Among methods for synthetic data generation are multi-agent dialogue generation, GAN-based text generation, speech synthesis using Tacotron, and back-translation for named entity recognition. Meanwhile, on the side of fine-tuning, the study looks at advancements including LoRA, quantized-LoRA, mBART, AraT5, Whisper, and SaudiBERT, focusing on domain-specific results of sentiment analysis, ASR, NLU, and summarization tasks.

Findings suggest that when relied upon alongside appropriate fine-tuning methods, synthetic corpora can dramatically enhance model performance in dialect-sensitive tasks. The emphasis, however, is placed on the ever-existing problems of generalizability, benchmark standardization, and ethical concerns on overfitting and reproducibility.

This paper introduces a classification scheme for synthetic data methods and fine-tuning techniques, together with a set of practice recommendations for researchers and developers in low-resource and dialectal NLP. In the final analysis, it argues for an inclusive Arabic NLP that highlights dialect diversity through scalable, intelligent data augmentation.