786 citations · 832 across the 4 of their papers we have counts for
6 papers · 1 filter
Language Models in the Loop: Incorporating Prompting into Weak Supervision
Ryan Smith, Jason A. Fries, Braden Hancock +1
We propose a new strategy for applying large pre-trained language models to novel tasks when labeled training data is limited. Rather than apply the model in a typical zero-shot or…
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
Stephen H. Bach, Victor Sanh, Zheng-Xin Yong +24
PromptSource is a system for creating, sharing, and using natural language prompts. Prompts are functions that map an example from a dataset to a natural language input and target…
What will it take to generate fairness-preserving explanations?
Jessica Dai, Sohini Upadhyay, Stephen H. Bach +1
In situations where explanations of black-box models may be useful, the fairness of the black-box is also often a relevant concern. However, the link between the fairness of the bl…
Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks
Reza Esfandiarpoor, Amy Pu, Mohsen Hajabdollahi +1
In many practical few-shot learning problems, even though labeled examples are scarce, there are abundant auxiliary datasets that potentially contain useful information. We propose…
Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale
Stephen H. Bach, Daniel Rodriguez, Yintao Liu +10
Labeling training data is one of the most costly bottlenecks in developing machine learning-based applications. We present a first-of-its-kind study showing how existing knowledge…
Snorkel: Rapid Training Data Creation with Weak Supervision
Alexander Ratner, Stephen H. Bach, Henry Ehrenberg +3
Labeling training data is increasingly the largest bottleneck in deploying machine learning systems. We present Snorkel, a first-of-its-kind system that enables users to train stat…