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The Divergence Hypothesis: Unmasking Lexical Interference and Label Bias in Mental Health NLP

This story is from 2026-08-24. It is preserved in the archive; the latest stories are on the live feed.

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 linguistic signals. We introduce TSS (Triple-Stream Stress probe), a multi-channel diagnos…

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  1. 2026-08-24 04:00 · arXiv cs.CL
    The Divergence Hypothesis: Unmasking Lexical Interference and Label Bias in Mental Health NLP

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