Personalized Prediction of Perceived Message Effectiveness Using Large Language Model Based Digital Twins

The study evaluates large language models' ability to predict perceived message effectiveness (PME) for personalized smoking cessation messages on mobile platforms. Digital twin models that incorporate individual characteristics outperform other methods by 12-13 percentage points in accuracy, suggesting potential for more tailored health interventions. Content creators should consider using LLM-based digital twins to personalize content based on user profiles and past interactions.

Read the full article at arXiv cs.CL (NLP)

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