Fakhri Karray

Small Language Models can achieve robust reinforcement learning performance by focusing on model quality and reward signal rather than sheer parameter count.

Multimodal unlearning could revolutionize how we handle sensitive data in AI, enabling targeted removal without sacrificing model performance.

Redefining stance detection as masked language modeling allows for competitive performance with minimal architectural changes, even in low-resource settings.

A task-specific, lightweight transformer can outperform state-of-the-art reasoning LLMs and commercial tools in C code vulnerability detection, at a fraction of the inference cost.