ARTICLE: Annotator Reliability Through In-Context Learning

TL;DR

This paper introduces ARTICLE, an in-context learning framework that assesses annotator reliability by leveraging self-consistency, aiming to enhance data quality in subjective NLP tasks like offensive speech detection.

Contribution

The paper proposes a novel ICL-based method for evaluating annotator quality, addressing subjectivity and disagreement issues in NLP annotations.

Findings

ARTICLE effectively identifies reliable annotators.

The framework outperforms traditional quality assessment methods.

It improves data quality in subjective NLP tasks.

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 \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.