Proposes a modular architecture for LLM-based wellbeing recommenders using explicit constraints on guidance, explanations, directness, and user control to address trust calibration, intent alignment, and consequence awareness.
Said, On explaining recommendations with Large Language Models: a review, Frontiers in Big Data 7 (2025)
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Designing Trustworthy LLM-based Wellbeing Recommendation through Controllable Interaction
Proposes a modular architecture for LLM-based wellbeing recommenders using explicit constraints on guidance, explanations, directness, and user control to address trust calibration, intent alignment, and consequence awareness.