ARTICLE: Annotator Reliability Through In-Context Learning
This addresses the challenge of ensuring data quality in subjective annotation tasks for NLP researchers and practitioners, though it appears incremental as it builds on existing in-context learning methods.
The paper tackles the problem of assessing annotator quality in subjective NLP tasks like offensive speech detection by proposing ARTICLE, an in-context learning framework that estimates quality through self-consistency, and finds it robust for identifying reliable annotators to improve data quality.
Ensuring annotator quality in training and evaluation data is a key piece of machine learning in NLP. Tasks such as sentiment analysis and offensive speech detection are intrinsically subjective, creating a challenging scenario for traditional quality assessment approaches because it is hard to distinguish disagreement due to poor work from that due to differences of opinions between sincere annotators. With the goal of increasing diverse perspectives in annotation while ensuring consistency, we propose \texttt{ARTICLE}, an in-context learning (ICL) framework to estimate annotation quality through self-consistency. We evaluate this framework on two offensive speech datasets using multiple LLMs and compare its performance with traditional methods. Our findings indicate that \texttt{ARTICLE} can be used as a robust method for identifying reliable annotators, hence improving data quality.