Fine-Tuning Arabic Large Language Models for improved multi-turn dialogue: A blueprint for synthetic data generation and benchmarking

The rapid evolution of Large Language Models (LLMs) has fueled increasing interest in developing Arabic conversational systems capable of sustaining coherent multi-turn dialogues. However, progress remains constrained by the scarcity of large-scale, diverse, and high-quality datasets specifically designed for Arabic multi-turn interaction. This study presents a reproducible methodology for constructing such a dataset through structured prompting of an instruction-tuned Arabic LLM (Jais-13b-chat), yielding 43,316 multi-turn conversations across 93 topics and 151 countries. Two pre-trained Arabic language models (ArabianGPT-08B-V2 and AraGPT2-mega) were fine-tuned on this synthetic data and benchmarked against multilingual instruction-tuned baselines using a comprehensive evaluation framework combining automatic metrics (Perplexity and RAVEN) with structured human evaluation. Fine-tuned ArabianGPT-08B-V2 achieved the highest RAVEN score (0.823) for cross-model comparison, outperforming both fine-tuned AraGPT2-mega and instruction-tuned baselines while maintaining strong within-model perplexity (9.4). Human evaluation by two independent raters demonstrated acceptable inter-rater reliability (Cohen’s κ = 0.229–0.739) with positive rank correlations (Spearman ρ = 0.424–0.759), yielding overall quality scores of 4.04–4.34 on a five-point scale. These findings demonstrate that high-quality, LLM-generated synthetic data effectively improves Arabic conversational models, providing a scalable, resource-efficient blueprint for dialogue systems in low-resource and culturally specific settings.