Recently, Miller et al. showed that a model's in-distribution (ID) accuracy has a strong linear
correlation with its out-of-distribution (OOD) accuracy, on several OOD benchmarks, a …

Deep neural networks excel at learning from labeled data and achieve state-of-the-art
resultson a wide array of Natural Language Processing tasks. In contrast, learning from …

We empirically show that the test error of deep networks can be estimated by simply training
the same architecture on the same training set but with a different run of Stochastic Gradient …

In recent years, Natural Language Processing (NLP) models have achieved phenomenal
success in linguistic and semantic tasks like text classification, machine translation, cognitive …

When a deep learning model is deployed in the wild, it can encounter test data drawn from
distributions different from the training data distribution and suffer drop in performance. For …

Keeping the performance of language technologies optimal as time passes is of great
practical interest. We study temporal effects on model performance on downstream …

Evaluating the factual consistency of automatically generated summaries is essential for the
progress and adoption of reliable summarization systems. Despite recent advances, existing …

Mood inference with mobile sensing data has been studied in ubicomp literature over the
last decade. This inference enables context-aware and personalized user experiences in …

We propose a metric—Projection Norm—to predict a model's performance on out-of-
distribution (OOD) data without access to ground truth labels. Projection Norm first uses …

In order to equip NLP systems with selective prediction capability, several task-specific
approaches have been proposed. However, which approaches work best across tasks or …