Crowd Label

Crowd labeling leverages multiple annotators to label data for machine learning, but inherent noise from annotator inconsistencies necessitates robust aggregation methods. Current research focuses on improving label accuracy through advanced aggregation algorithms, including those incorporating large language models (LLMs) to complement or enhance human-generated labels, and on integrating logical constraints to guide the learning process. These advancements are crucial for building reliable and high-performing AI systems, particularly in applications where data privacy is a critical concern, as demonstrated by recent work exploring privacy-preserving labeling techniques.