Conformal Prediction for Natural Language Processing

Natural language processing (NLP) grapples with the complexities of human language, where uncertainty is an inherent challenge. As NLP models become integral to risk-sensitive and critical applications, ensuring their reliability is paramount. Conformal prediction emerges as a promising technique, offering a way to quantify the trustworthiness of these models’ predictions, particularly when faced with miscalibrated outputs from deep learning models.

In this chapter, we will navigate the NLP conformal prediction world, understand its significance, and learn how to harness its power for more reliable and confident predictions.

In this chapter, we’re going to cover the following main topics:

• Uncertainty quantification for NLP
• Why deep learning produces miscalibrated predictions
• Various approaches to quantify uncertainty in NLP problems
• Conformal prediction for NLP
• Building NLP classifiers...