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.
L e W i D i-2025 at NLP erspectives: The Third Edition of the Learning with Disagreements Shared Task
2 Pith papers cite this work. Polarity classification is still indexing.
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A late-fusion gradient-boosting pipeline with LLM semantic features is submitted to the EXIST 2026 lab for sexism identification in memes and videos, showing mixed generalization from development to test data.
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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.
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Multimodal Sexism Identification and Characterization using Large Language Models and Gradient Boosting
A late-fusion gradient-boosting pipeline with LLM semantic features is submitted to the EXIST 2026 lab for sexism identification in memes and videos, showing mixed generalization from development to test data.