Various approaches to quantify uncertainty in NLP problems
Multiple methods to quantify uncertainty in NLP problems have been explored to address the challenges of miscalibration and language’s inherent unpredictability.
We will now look at Bayesian approaches to UQ.
Bayesian approaches to uncertainty quantification
Bayesian methods provide a framework for modeling uncertainty. By treating model parameters as distributions rather than fixed values, Bayesian neural networks offer a measure of uncertainty associated with predictions. This probabilistic approach ensures that the model not only gives an estimate but also conveys the confidence or spread of that estimate.
These are some of the examples of Bayesian approaches to UQ.
- Variational inference is a technique to approximate the posterior distribution of the model parameters, enabling the network to output distributions for predictions.
- Bayesian neural networks (BNNs) are neural networks with weights...
