ACL Findings2026
DemMA: Dementia Multi-Turn Dialogue Agent with Expert-Guided Reasoning and Action Simulation
Yutong Song, Jiang Wu, Kazi Shaharair Sharif, Pengfei Zhang, Wenjun Huang, Honghui Xu, Nikil D. Dutt, Amir M. Rahmani
摘要
Simulating dementia patients with large language models (LLMs) is challenging due to the need to jointly model cognitive impairment, emotional dynamics, and nonverbal behaviors over long conversations. We present DemMA, an expert-guided dementia dialogue agent for high-fidelity multi-turn patient simulation. DemMA constructs clinically grounded dementia personas by integrating pathology information, personality traits, and subtypespecific memory-status personas informed by clinical experts. To move beyond text-only simulation, DemMA explicitly models nonverbal behaviors, including motion, facial expressions, and vocal cues. We further introduce a Chainof-Thought distillation framework that trains a single LLM to jointly generate reasoning traces, patient utterances, and aligned behavioral actions within one forward pass, enabling efficient deployment without multi-agent inference.
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