335 citations · 480 across the 8 of their papers we have counts for
10 papers · 1 filter
Towards Expert-Level Medical Question Answering with Large Language Models
Karan Singhal, Tao Tu, Juraj Gottweis +28
Recent artificial intelligence (AI) systems have reached milestones in "grand challenges" ranging from Go to protein-folding. The capability to retrieve medical knowledge, reason o…
Meta-Learning Fast Weight Language Models
Kevin Clark, Kelvin Guu, Ming-Wei Chang +3
Dynamic evaluation of language models (LMs) adapts model parameters at test time using gradient information from previous tokens and substantially improves LM performance. However,…
Pre-Training Transformers as Energy-Based Cloze Models
Kevin Clark, Minh-Thang Luong, Quoc V. Le +1
We introduce Electric, an energy-based cloze model for representation learning over text. Like BERT, it is a conditional generative model of tokens given their contexts. However, E…
BAM! Born-Again Multi-Task Networks for Natural Language Understanding
Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal +2
It can be challenging to train multi-task neural networks that outperform or even match their single-task counterparts. To help address this, we propose using knowledge distillatio…
What Does BERT Look At? An Analysis of BERT's Attention
Kevin Clark, Urvashi Khandelwal, Omer Levy +1
Large pre-trained neural networks such as BERT have had great recent success in NLP, motivating a growing body of research investigating what aspects of language they are able to l…
Sample Efficient Text Summarization Using a Single Pre-Trained Transformer
Urvashi Khandelwal, Kevin Clark, Dan Jurafsky +1
Language model (LM) pre-training has resulted in impressive performance and sample efficiency on a variety of language understanding tasks. However, it remains unclear how to best…
Semi-Supervised Sequence Modeling with Cross-View Training
Kevin Clark, Minh-Thang Luong, Christopher D. Manning +1
Unsupervised representation learning algorithms such as word2vec and ELMo improve the accuracy of many supervised NLP models, mainly because they can take advantage of large amount…
Deep Reinforcement Learning for Mention-Ranking Coreference Models
Kevin Clark, Christopher D. Manning
Coreference resolution systems are typically trained with heuristic loss functions that require careful tuning. In this paper we instead apply reinforcement learning to directly op…
Improving Coreference Resolution by Learning Entity-Level Distributed Representations
Kevin Clark, Christopher D. Manning
A long-standing challenge in coreference resolution has been the incorporation of entity-level information - features defined over clusters of mentions instead of mention pairs. We…
Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora
William L. Hamilton, Kevin Clark, Jure Leskovec +1
A word's sentiment depends on the domain in which it is used. Computational social science research thus requires sentiment lexicons that are specific to the domains being studied.…