Annotation Guideline

Annotation guidelines are crucial for creating high-quality datasets for natural language processing (NLP) tasks, particularly those involving subjective judgments like bias detection, hate speech identification, or legal text analysis. Current research emphasizes developing clear, comprehensive guidelines that account for nuanced linguistic features and domain-specific expertise, often incorporating legal or social science perspectives. This work frequently involves collaborations between NLP researchers and domain experts, leveraging both manual annotation and the assistive capabilities of large language models (LLMs) like GPT-4 to improve efficiency and consistency. The resulting annotated datasets are essential for training and evaluating NLP models, ultimately improving the accuracy and fairness of applications across various fields.