LLM agents outperform humans in romance-baiting scams, eliciting greater trust and 46% compliance versus 18%, with 0% detection by safety filters and 87% of scam tasks automatable.
Can ai relate: Testing large language model response for mental health support
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
representative citing papers
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.
Creates a clinical crisis taxonomy and 2,252-example dataset then audits five LLMs, finding variable safety with notable failures on indirect signals and in self-harm categories.
LLMs detect social signals in clinical transcripts across model families, with an agreement-weighted ensemble using group-level agreement patterns improving accuracy and stability over individual models.
citing papers explorer
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Love, Lies, and Language Models: Investigating AI's Role in Romance-Baiting Scams
LLM agents outperform humans in romance-baiting scams, eliciting greater trust and 46% compliance versus 18%, with 0% detection by safety filters and 87% of scam tasks automatable.
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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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Between Help and Harm: An Evaluation of Mental Health Crisis Handling by LLMs
Creates a clinical crisis taxonomy and 2,252-example dataset then audits five LLMs, finding variable safety with notable failures on indirect signals and in self-harm categories.
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SocialLM: Social Signal Processing of Patient-Provider Communication using LLMs and Contextual Aggregation
LLMs detect social signals in clinical transcripts across model families, with an agreement-weighted ensemble using group-level agreement patterns improving accuracy and stability over individual models.