The discussion delves into the challenges of uncertainty quantification as models grow deeper and more complex. With large language models, the lack of accessible training data complicates the ability to assess performance. Recent research explores innovative techniques like snapshot ensembles and dropout to gauge uncertainty, revealing a balance between practicality and effectiveness in model evaluation.
