The Ghost Annotator: a Framework to Explore Human Label Variation in Content Moderation through Conformal Prediction

arXiv:2606.02911v1 Announce Type: new Abstract: Current research primarily focuses on model performance, while comparatively less attention has been devoted to uncertainty estimation, particularly in settings where LLMs are increasingly used to generate annotated data. We introduce a framework combining conformal prediction with Collaborative Filtering-style annotators' representation to model LLM behavior in relation to human annotators and to analyze patterns of agreement and disagreement. Using Non-Conformity Scores, we introduce the Ghost Prediction metric and the Ghost Annotator represent

Source: arXiv cs.CL — read the full report at the original publisher.

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