Annotation Study

Annotation studies focus on creating high-quality labeled datasets for training machine learning models, particularly in natural language processing. Current research emphasizes improving annotation efficiency and accuracy through techniques like model-in-the-loop approaches, active learning, and sophisticated worker selection algorithms, often incorporating large language models to assist or augment human annotators. These studies are crucial for advancing the reliability and fairness of AI systems, impacting various fields from healthcare (reducing bias in electronic health records) to legal proceedings (mitigating gender bias in trial transcripts) and improving the performance of NLP models across diverse tasks.