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Recent Weak Supervision (WS) approaches have had widespread success in easingthe bottleneck of labeling training data for machine learning by synthesizinglabels from multiple potentially noisy supervision sources. However, propermeasurement and analysis of these approaches remain a challenge. First,datasets used in existing works are often private and/or custom, limitingstandardization. Second, WS datasets with the same name and base data oftenvary in terms of the labels and weak supervision sources used, a significant"hidden" source of evaluation variance. Finally, WS studies often diverge interms of the evaluation protocol and ablations used. To address these problems,we introduce a benchmark platform, WRENCH, for thorough and standardizedevaluation of WS approaches. It consists of 22 varied real-world datasets forclassification and sequence tagging; a range of real, synthetic, andprocedurally-generated weak supervision sources; and a modular, extensibleframework for WS evaluation, including implementations for popular WS methods.We use WRENCH to conduct extensive comparisons over more than 120 methodvariants to demonstrate its efficacy as a benchmark platform. The code isavailable at https://github.com/JieyuZ2/wrench.

WRENCH: A Comprehensive Benchmark for Weak Supervision