SDNLP at AMIYA 2026: Syrian Arabic Dialect Modeling with LoRA

AbstractDialectal Arabic continues to represent a persistent challenge for contemporary large language models, which are predominantly trained and optimized for Modern Standard Arabic (MSA) and therefore exhibit limited capability when processing colloquial varieties. In this study, a dedicated system developed for participation in the AMIYA shared task focusing on Syrian Arabic is presented. The proposed solution is based on the integration of parameter-efficient fine-tuning through Low-Rank Adaptation (LoRA) with prompt-guided inference, aiming to enhance dialectal adequacy and linguistic naturalness. Rather than emphasizing strict factual precision, the system is deliberately designed to prioritize fluent and authentic Syrian Arabic generation, in accordance with the evaluation principles adopted by the AL-QASIDA benchmark. This design choice reflects a focus on human-perceived language quality and dialectal fidelity, which are central to effective dialect-aware language modeling.