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Cited By

Don't Blame the Data, Blame the Model: Understanding Noise and Bias When
Learning from Subjective Annotations

Don't Blame the Data, Blame the Model: Understanding Noise and Bias When Learning from Subjective Annotations

6 March 2024

Negar Mokhberian

Prathyusha Naresh Kumar

Fred Morstatter

Kristina Lerman

ArXiv (abs)PDF HTML

Papers citing "Don't Blame the Data, Blame the Model: Understanding Noise and Bias When Learning from Subjective Annotations"

5 / 5 papers shown

Are you sure? Measuring models bias in content moderation through uncertainty

Are you sure? Measuring models bias in content moderation through uncertainty

Alessandra Urbinati

193

0

0

21 Sep 2025

Are Humans as Brittle as Large Language Models?

Are Humans as Brittle as Large Language Models?Neural Information Processing Systems (NeurIPS), 2024

205

0

0

09 Sep 2025

Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions

Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text PerceptionsAnnual Meeting of the Association for Computational Linguistics (ACL), 2025

Matthias Orlikowski

Philipp Cimiano

343

23

0

28 Feb 2025

Behind Closed Words: Creating and Investigating the forePLay Annotated Dataset for Polish Erotic Discourse

Behind Closed Words: Creating and Investigating the forePLay Annotated Dataset for Polish Erotic DiscourseAnnual Meeting of the Association for Computational Linguistics (ACL), 2024

Katarzyna Lorenc

Agnieszka Karlinska

231

0

0

23 Dec 2024

Label Convergence: Defining an Upper Performance Bound in Object Recognition through Contradictory Annotations

Label Convergence: Defining an Upper Performance Bound in Object Recognition through Contradictory AnnotationsIEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2024

David Tschirschwitz

Volker Rodehorst

284

2

0

14 Sep 2024