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cs.CL2026
Reliable but Design-Sensitive: Instrument Uncertainty in LLM Annotation
Thomas Reiter, Christoph Kern, Fedor Miasnikov +4
Large language models (LLMs) can give reliable labels under one setup yet change those labels when researchers make other reasonable design choices. We tested seven LLMs, 12 task d…
stat.ME2026
From Ground Truth to Measurement: A Statistical Framework for Human Labeling
Robert Chew, Stephanie Eckman, Christoph Kern +1
Supervised machine learning assumes that labeled data provide accurate measurements of the concepts models are meant to learn. Yet in practice, human labeling introduces systematic…