TheraJudge, trained via preference optimization on human annotations, reaches high clinician agreement (ICC 0.87-0.95) and, when used by TheraAgent, raises human-rated therapeutic quality by 0.43 points on a 5-point scale with 94% recovery of low-quality responses.
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2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CL 2years
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UNVERDICTED 2representative citing papers
KGR, a constrained LLM for generating keyphrases from crisis SMS, expands a static taxonomy and raises topic-retrieval accuracy from 0.25 to 0.70 while surfacing new themes like immigration problems.
citing papers explorer
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Training Therapeutic Judges and Multi-Agent Systems for Human-Aligned Mental Health Support
TheraJudge, trained via preference optimization on human annotations, reaches high clinician agreement (ICC 0.87-0.95) and, when used by TheraAgent, raises human-rated therapeutic quality by 0.43 points on a 5-point scale with 94% recovery of low-quality responses.
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Keyphrase Generative Representation of Youth Crisis Conversations Beyond Static Taxonomies
KGR, a constrained LLM for generating keyphrases from crisis SMS, expands a static taxonomy and raises topic-retrieval accuracy from 0.25 to 0.70 while surfacing new themes like immigration problems.