The Ghost Annotator framework applies conformal prediction and collaborative filtering representations to measure LLM divergence from human annotations across four models and datasets, revealing higher confidence in misaligned cases and consistent demographic misalignment.
Springer US, New York, NY
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The thesis identifies theoretical, empirical, and conceptual flaws in offline fairness measures for recommender systems and contributes new evaluation methods and practical guidelines.
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The Ghost Annotator: a Framework to Explore Human Label Variation in Content Moderation through Conformal Prediction
The Ghost Annotator framework applies conformal prediction and collaborative filtering representations to measure LLM divergence from human annotations across four models and datasets, revealing higher confidence in misaligned cases and consistent demographic misalignment.