AI RESEARCH
Validation of a Small Language Model for DSM-5 Substance Category Classification in Child Welfare Records
arXiv CS.CL
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ArXi:2603.06836v1 Announce Type: new Background: Recent studies have nstrated that large language models (LLMs) can perform binary classification tasks on child welfare narratives, detecting the presence or absence of constructs such as substance-related problems, domestic violence, and firearms involvement. Whether smaller, locally deployable models can move beyond binary detection to classify specific substance types from these narratives remains untested.