Uncertainty quantification for NLP
Uncertainty quantification in NLP is an essential yet often overlooked aspect of model development and deployment. As NLP models become increasingly integrated into critical applications—from healthcare diagnostics to financial predictions—the need to understand and convey the confidence level of their outputs becomes paramount. Uncertainty quantification provides a framework for assessing the reliability of predictions, allowing users and developers to gauge the model’s decisiveness and the potential risks of relying on its results. This section delves into the importance, methodologies, and practical considerations of uncertainty quantification in NLP, highlighting its pivotal role in building robust and trustworthy language models.
We will now explore uncertainty in NLP and the benefits and challenges of quantifying uncertainty in NLP applications.
What is uncertainty in NLP?
NLP, at its core, is about making sense...
