NLP

Subasa - Adapting Language Models for Low-resourced Offensive Language Detection in Sinhala

ARTICLE: Annotator Reliability Through In-Context Learning

Rater Cohesion and Quality from a Vicarious Perspective

Harnessing Business and Media Insights with Large Language Models

Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive

Workflow Diagram for VOICED

Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful CrowdOpinion with Population-level Learning

Disagreement Matters: Preserving Label Diversity by Jointly Modeling Item and Annotator Label Distributions with DisCo

Findings from the Bambara - French Machine Translation Competition (BFMT 2023)

Improving Label Quality by Joint Probabilistic Modeling of Items and Annotators

Annotator Response Distributions as a Sampling Frame