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University of Illinois Urbana-Champaign
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Open-source LLMs, trained with Reddit community feedback, can match the performance of proprietary models in generating supportive mental health responses, offering a privacy-preserving alternative.
LLMs morph into riskier conversationalists when playing directive support roles like "Coach" or "Inform" for caregivers, revealing a troubling quality-safety trade-off.
Content moderation systems, designed to protect users, may inadvertently cripple LLMs' ability to function as effective therapists by censoring crucial topics in real therapy sessions.
AI negotiation coaches don't work for everyone: your personality determines whether you'll benefit from AI or a good old-fashioned handbook.
LLMs used in matchmaking amplify existing caste hierarchies, rating same-caste matches significantly higher and perpetuating social biases in sensitive relational domains.
AI-agent communities aren't just pale imitations of human ones; they're structurally and linguistically distinct, exhibiting extreme inequality and homogenization driven by identifiable agent-level stylistic outliers.
Generative search engines create "answer bubbles" by selectively citing and framing information, leading to divergent information realities compared to traditional search.
