The Divergence Hypothesis: Unmasking lexical interference and label bias in mental health NLP
Read the original at arxiv.org→arXiv:2608.20353v1 Announce Type: new Abstract: Computational mental health (CMH) classifiers often degrade under distribution shift because human annotators and distant-supervision pipelines reward different...
Original headline: "The Divergence Hypothesis: Unmasking Lexical Interference and Label Bias in Mental Health NLP"
Coverage timeline
- Aug 24, 04:00 UTC arXiv cs.CL lead source The Divergence Hypothesis: Unmasking Lexical Interference and Label Bias in Mental Health NLP