AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, the potential large-scale risks associated with misaligned AI …

Attention is an increasingly popular mechanism used in a wide range of neural
architectures. The mechanism itself has been realized in a variety of formats. However …

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 …

Attention Model has now become an important concept in neural networks that has been
researched within diverse application domains. This survey provides a structured and …

The Transformer is a sequence model that forgoes traditional recurrent architectures in favor
of a fully attention-based approach. Besides improving performance, an advantage of using …

Despite their success, deep networks are used as black-box models with outputs that are not
easily explainable during the learning and the prediction phases. This lack of interpretability …

The Transformer is a fully attention-based alternative to recurrent networks that has
achieved state-of-the-art results across a range of NLP tasks. In this paper, we analyze the …

Transformer models are revolutionizing machine learning, but their inner workings remain
mysterious. In this work, we present a new visualization technique designed to help …

Neural NLP models are increasingly accurate but are imperfect and opaque---they break in
counterintuitive ways and leave end users puzzled at their behavior. Model interpretation …

As the demand for explainable deep learning grows in the evaluation of language
technologies, the value of a principled grounding for those explanations grows as well. Here …