Friday 10:00-11:30: Data and Model Quality Evaluation

Understanding model performance, finding gaps in quality and iteratively improving models

and data sets. Open questions around truthfulness, deceptiveness and groundedness of model

outputs, differing behavior of large models in training, testing and production environments.