Xiaoni Duan

Xiaoni Duan's research focuses on crowdsourcing and data annotation, including how to improve the accuracy of classification tasks performed by crowds of human workers, how task design affects the quality of both objective and subjective annotations, and more recently whether large language models (LLMs) can serve as reliable substitutes for human annotators. This work sits at the intersection of human-computer interaction, machine learning, and data quality, and is relevant to students interested in how data used to train AI systems is collected and validated.

Publication output was sparse and irregular for most of the last decade (often zero or one paper per year) but shows a sharp recent increase, with five records in 2026.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026