Structure Discovery in Prompted Weak Supervision

Prompted weak supervision (PromptedWS) applies pre-trained large language models (LLMs) as supervision sources in a weak supervision setup to efficiently distill information from LLMs and obtain labeled datasets at scale. We further extend the use of LLMs to address one of the key challenges in weak supervision: learning the dependency structure among noisy supervision sources. In this work, we highlight the challenge of structure discovery in PromptedWS. We propose a Structure Refining Module, a simple yet effective first approach based on the similarities of the prompts by taking advantage of the intrinsic structure in the embedding space. At the core of our method are Labeling Function Removal (LaRe) and Correlation Structure Generation (CosGen). Compared to previous methods that learn the dependencies from weak labels, our method finds the dependencies that are intrinsic in the embedding space. We show that Structure Refining Module improves the PromptedWS by up to 12.7 points on benchmark tasks.