AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels

TL;DR

AutoWS-Bench-101 evaluates automated weak supervision techniques across diverse challenging domains, comparing their effectiveness with zero-shot and few-shot methods, and highlights the importance of integrating foundation model signals for improved performance.

Contribution

Introduces a comprehensive benchmark framework for automated weak supervision in complex domains, facilitating comparison with modern zero-shot and few-shot learners.

Findings

AutoWS methods often need foundation model signals to outperform simple baselines.

Benchmark reveals the importance of integrating foundation models in AutoWS.

Thorough ablation studies identify key factors influencing AutoWS performance.

Abstract

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 reveals 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 in many settings, 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.