We present Sparrow, an information-seeking dialogue agent trained to be ...
Large language models produce human-like text that drive a growing numbe...
Programming is a powerful and ubiquitous problem-solving tool. Developin...
This paper aims to help structure the risk landscape associated with
lar...
Large language models (LM) generate remarkably fluent text and can be
ef...
Recent research has made the surprising finding that state-of-the-art de...
In this paper we propose to augment a modern neural-network architecture...
Recent improvements in large-scale language models have driven progress ...
This paper considers the problem of efficient exploration of unseen
envi...
Adversarial testing methods based on Projected Gradient Descent (PGD) ar...
Neural networks are part of many contemporary NLP systems, yet their
emp...
Recent work has uncovered the interesting (and somewhat surprising) find...
Models such as Sequence-to-Sequence and Image-to-Sequence are widely use...
In this paper, we propose Neural Phrase-to-Phrase Machine Translation
(N...
We consider the problem of neural semantic parsing, which translates nat...
We present a neural semantic parser that translatesnatural language ques...
In this paper, we investigate the use of discourse-aware rewards with
re...
In conventional supervised training, a model is trained to fit all the
t...
Learning to walk over a graph towards a target node for a given input qu...
We develop a technique for transfer learning in machine comprehension (M...
In this paper, we present Neural Phrase-based Machine Translation (NPMT)...
Segmental structure is a common pattern in many types of sequences such ...
Recent studies on knowledge base completion, the task of recovering miss...
Teaching a computer to read and answer general questions pertaining to a...
Monaural source separation is important for many real world applications...