Summary

In the chapter, we have explored the inherent uncertainty challenges in the NLP domain. Recognizing the pivotal role of NLP models in today’s critical systems, the chapter emphasizes the importance of ensuring these models’ predictions are trustworthy and reliable. The chapter introduces conformal prediction as a solution to address the miscalibration seen in deep learning models’ outputs, offering a means to quantify the confidence of predictions robustly. Throughout this chapter, you gained insights into the intricacies of uncertainty quantification specific to NLP, the reasons why deep learning models often produce miscalibrated predictions, and various methods of quantifying uncertainty in NLP. Finally, we deeply studied the conformal prediction technique tailored for NLP tasks.

At the end of this chapter, you should have a holistic understanding of the challenges of uncertainty in NLP, the merits and mechanics of conformal prediction, and practical...