Computational simulation is increasingly relied upon for high-consequenc...
Currently, most machine learning models are trained by centralized teams...
Parameter-efficient methods are able to use a single frozen pre-trained ...
In this paper, we explore the challenging problem of performing a genera...
Recent neural network-based language models have benefited greatly from
...
As pre-trained language models have gotten larger, there has been growin...
This paper explores a simple method for improving the zero-shot learning...
Complex natural language understanding modules in dialog systems have a
...
In this work, we explore "prompt tuning", a simple yet effective mechani...
Many tasks in natural language processing, such as named entity recognit...
Current state-of-the-art models for named entity recognition (NER) are n...
Most state-of-the-art models in natural language processing (NLP) are ne...
Two competing file formats have become the de facto standards for
distri...
Current State-of-the-Art models in Named Entity Recognition (NER) are ne...
Because large, human-annotated datasets suffer from labeling errors, it ...