Liger, a combination of foundation models and weak supervision frameworks, improves existing weak supervision techniques by partitioning the embedding space and extending source votes in embedding space, resulting in improved performance on six benchmark NLP and video tasks.

This paper presents Nemo, an interactive system that improves the overall productivity of Weak Supervision learning pipelines by an average of 20%, compared to the prevailing WS approach.

This paper presents a comprehensive survey of recent advances in Programmatic Weak Supervision (PWS), and discusses related approaches to tackle limited labeled data scenarios.

This paper finds that only 13% of biomedical datasets are available via programmatic access and 30% lack documentation on licensing and permitted reuse, highlighting the dataset debt in biomedical NLP.

PromptSource is a system that provides a templating language, an interface, and a set of guidelines to create, share, and use natural language prompts to train and query language models.

In this paper, accepted at ICLR 2022, Chris and team at Stanford outline a new principled evaluation framework for comparing slice detection methods, then introduce a new technique motivated by our discoveries that outperforms existing methods by double digits.

This paper describes TAGLETS, a system built to study techniques for automatically exploiting all three types of data and creating high-quality, servable classifiers

Presenting Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules.