Shammur Absar Chowdhury

Culturally grounded multimodal evaluation reveals significant gaps in current AI systems' understanding of Arabic contexts, with implications for both accuracy and representation.

Fine-tuning outperforms zero-shot inference, but the real game-changer is the use of synthetic data to elevate performance in culturally specific tasks.

Current multimodal conversational AI systems excel in perception and action but falter in maintaining coherent dialogue across turns.

Achieving near-supervised performance in pronunciation assessment using only native speech resources could revolutionize how we evaluate language learners.