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

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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.

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Authors: Mirko Lai, Alessandra Urbinati, Simona Frenda, Fabiana Vernero, Marco Antonio Stranisci
Released: 2026-06-03 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because The Ghost Annotator: a Framework to Explore Human Label Variation in Content Moderation through Conformal Prediction overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, class, alignment, eval, rate, model. Source: arxiv cs.CL (NLP).
arXiv: 2606.02911

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