Conformal prediction for NLP

Conformal prediction is a flexible and statistically robust approach to uncertainty quantification. It is a distribution-free framework that can estimate uncertainty for machine learning models without requiring model retraining or access to limited APIs. The central idea behind conformal prediction is to output a set of predictions containing the correct output with a user-specified probability. Conformal prediction can help quantify the uncertainty associated with the model’s predictions in language models.

Conformal prediction is a framework that delivers valid confidence intervals for predictions, irrespective of the underlying machine learning model. In the NLP landscape, with its inherent challenges of ambiguity, context sensitivity, and linguistic diversity, conformal prediction offers a structured way to quantify uncertainty.

Validity and efficiency are the two fundamental principles of conformal prediction. Validity ensures that the...