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Poster
AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels
Nicholas Roberts · Xintong Li · Tzu-Heng Huang · Dyah Adila · Spencer Schoenberg · Cheng-Yu Liu · Lauren Pick · Haotian Ma · Aws Albarghouthi · Frederic Sala

Thu Dec 01 09:00 AM -- 11:00 AM (PST) @ Hall J #1029

Weak supervision (WS) is a powerful method to build labeled datasets for training supervised models in the face of little-to-no labeled data. It replaces hand-labeling data with aggregating multiple noisy-but-cheap label estimates expressed by labeling functions (LFs). While it has been used successfully in many domains, weak supervision's application scope is limited by the difficulty of constructing labeling functions for domains with complex or high-dimensional features. To address this, a handful of methods have proposed automating the LF design process using a small set of ground truth labels. In this work, we introduce AutoWS-Bench-101: a framework for evaluating automated WS (AutoWS) techniques in challenging WS settings---a set of diverse application domains on which it has been previously difficult or impossible to apply traditional WS techniques. While AutoWS is a promising direction toward expanding the application-scope of WS, the emergence of powerful methods such as zero-shot foundation models reveal the need to understand how AutoWS techniques compare or cooperate with modern zero-shot or few-shot learners. This informs the central question of AutoWS-Bench-101: given an initial set of 100 labels for each task, we ask whether a practitioner should use an AutoWS method to generate additional labels or use some simpler baseline, such as zero-shot predictions from a foundation model or supervised learning. We observe that it is necessary for AutoWS methods to incorporate signal from foundation models if they are to outperform simple few-shot baselines, and AutoWS-Bench-101 promotes future research in this direction. We conclude with a thorough ablation study of AutoWS methods.

Author Information

Nicholas Roberts (University of Wisconsin-Madison)

I am a Ph.D. student in CS at University of Wisconsin – Madison where I am advised by Fred Sala. Before that, I had the pleasure of working with Ameet Talwalkar and Zack Lipton during my MS at Carnegie Mellon University. As an undergraduate, I was extremely fortunate to work with both Sanjoy Dasgupta and Gary Cottrell at the University of California, San Diego. Before that, I was a community college student at Fresno City College, where I was lucky enough to learn calculus, linear algebra, AND C++ from Greg Jamison.

Xintong Li (University of Wisconsin Madison)
Tzu-Heng Huang (University of Wisconsin - Madison)
Dyah Adila (University of Wisconsin-Madison)
Spencer Schoenberg (Department of Computer Science, University of Wisconsin - Madison)
Cheng-Yu Liu (Columbia University)
Lauren Pick
Haotian Ma
Aws Albarghouthi (University of Wisconsin, Madison)
Frederic Sala (University of Wisconsin, Madison)

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