Uncertainty-aware multimodal affective data fusion for personalized mental health dialogue

Personalized affective dialogue systems are critical for mental health applications, where responses must be emotionally appropriate and tailored to individual users. However, most existing large language model (LLM) based approaches rely on deterministic personalization, ignore uncertainty in affective understanding, and are difficult to deploy under privacy constraints. In this paper, we propose PALLM, a personalized affective large language modeling framework designed for privacy-preserving mental health dialogue. PALLM decouples personalization into two complementary components: a deterministic personalization layer that captures stable user preferences, and a Bayesian affective representation layer that models dynamic emotional states and uncertainty. By restricting uncertainty modeling to affective representations rather than full LLM parameters, PALLM achieves efficient and scalable personalization under federated learning. Extensive experiments on EmpatheticDialogues and a real-world mental health conversation dataset show that PALLM improves affective alignment and robustness compared with non-personalized and partially personalized baselines.