Boundaries agree, labels do not: intra-annotator dynamics as a kind of training data
Read the original at arxiv.org→arXiv:2610.04370v1 Announce Type: new Abstract: Data quality now matters as much as compute for training language models. Much training data comes from human annotation of text, and interpretive annotation has no...
Original headline: "Boundaries Agree, Labels Do Not: Intra-Annotator Dynamics as a Kind of Training Data"
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- Oct 6, 04:00 UTC arXiv cs.CL lead source Boundaries Agree, Labels Do Not: Intra-Annotator Dynamics as a Kind of Training Data