The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

Recently, there has been a surge of interest in the NLP community on the use of pretrained
Language Models (LMs) as Knowledge Bases (KBs). Researchers have shown that LMs …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

Finetuning language models on a collection of datasets phrased as instructions has been
shown to improve model performance and generalization to unseen tasks. In this paper we …

Consistency of a model—that is, the invariance of its behavior under meaning-preserving
alternations in its input—is a highly desirable property in natural language processing. In …

The advent of large language models (LLMs) has revolutionized natural language
processing, enabling the generation of coherent and contextually relevant text. As LLMs …

The last decade of machine learning has seen drastic increases in scale and capabilities.
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …

Deep neural networks (DNNs) are an indispensable machine learning tool despite the
difficulty of diagnosing what aspects of a model's input drive its decisions. In countless real …

We illustrate how standard psychometric inventories originally designed for assessing
noncognitive human traits can be repurposed as diagnostic tools to evaluate analogous …

Artificial Intelligence techniques powered by deep neural nets have achieved much success
in several application domains, most significantly and notably in the Computer Vision …