Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …

Adversarial evaluation stress-tests a model's understanding of natural language. Because
past approaches expose superficial patterns, the resulting adversarial examples are limited …

Machine learning is an important tool for decision making, but its ethical and responsible
application requires rigorous vetting of its interpretability and utility: an understudied …

Deep neural networks (DNNs) have achieved remarkable success in various tasks (eg,
image classification, speech recognition, and natural language processing (NLP)). However …

Deep neural networks (DNNs) have achieved remarkable success in various tasks (eg,
image classification, speech recognition, and natural language processing (NLP)). However …

When answering complex questions, large language models (LLMs) may produce answers
that do not satisfy all criteria of the question. While existing self-evaluation techniques aim to …

Natural language processing systems are often downstream of unreliable inputs: machine
translation, optical character recognition, or speech recognition. For instance, virtual …

Modern question answering systems have been touted as approaching human
performance. However, existing question answering datasets are imperfect tests. Questions …

Humans ask questions to learn about the world and to test knowledge understanding. The
ability to ask questions combines aspects of intelligence unique to humans: language …

Natural language processing needs substantial data to make robust predictions. Automatic
methods, unspecialized crowds, and domain experts can be used to collect conversational …