Challenges in Multilingual models
In the context of multilingual language models, understanding and quantifying uncertainty is crucial for building reliable AI systems. Confidence estimation allows us to gauge how much trust we can place in a model’s predictions, while calibration ensures that these confidence scores accurately reflect the true likelihood of correctness. Techniques such as conformal prediction provide a principled framework for generating reliable confidence measures, enabling more robust and interpretable model outputs across diverse languages and tasks.
Conformal prediction is a statistical framework that provides confidence measures for model predictions by constructing prediction sets that are guaranteed to contain the true outcome with a user-specified probability, under minimal assumptions. This approach enables models to quantify uncertainty in their outputs, making them more reliable and interpretable, especially in multilingual settings where data distribution may vary.