3 papers
cs.LG2026
An Assessment of Human vs. Model Uncertainty in Soft-Label Learning and Calibration
Maja Pavlovic, Silviu Paun, Massimo Poesio
Central to human-aligned AI is understanding the benefits of human-elicited labels over synthetic alternatives. While human soft-labels improve calibration by capturing uncertainty…
cs.CL2026
Human Label Variation in Implicit Discourse Relation Recognition
Frances Yung, Daniil Ignatev, Merel Scholman +2
There is growing recognition that many NLP tasks lack a single ground truth, as human judgments reflect diverse perspectives. To capture this variation, models have been developed…
cs.CL2025
Assessing the Reliability of LLMs Annotations in the Context of Demographic Bias and Model Explanation
Hadi Mohammadi, Tina Shahedi, Pablo Mosteiro +3
Understanding the sources of variability in annotations is crucial for developing fair NLP systems, especially for tasks like sexism detection where demographic bias is a concern.…