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ML Research Contributor for Qatar Computing Research Institute (QCRI)
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Aslema, the system for NADI 2026 Shared Task 5, which consists of two subtasks: intent recognition and slot filling and four omni LLMs in a zero-shot setting are presented and it is shown that fine-tuning consistently out-performs zero-shot inference.
This work evaluates four omni LLMs in a zero-shot setting and shows that fine-tuning consistently outperforms zero-shot inference, and explores synthetic data augmentation by using an LLM to generate culturally grounded Tunisian Derja utterances, followed by voice cloning to generate synthetic speech.
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