ARTICLE: Annotator Reliability Through In-Context Learning (Student Abstract)

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 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 ARTICLE can be used as a robust method for identifying reliable annotators, hence improving data quality.

Authors

• Sujan Dutta Rochester Institute of Technology
• Deepak Pandita Rochester Institute of Technology
• Tharindu Cyril Weerasooriya Rochester Institute of Technology
• Marcos Zampieri George Mason University
• Christopher M. Homan Rochester Institute of Technology
• Ashiqur R. KhudaBukhsh Rochester Institute of Technology

Keywords

No keywords are indexed for this paper.