基于原则的自适应策略用于认知障碍老年人群体认知刺激对话
A Principle-Driven Adaptive Policy for Group Cognitive Stimulation Dialogue for Elderly with Cognitive Impairment
AI总结：
本文提出基于原则的自适应策略，通过GCSD系统解决认知障碍老年人群体对话中的认知刺激问题，提升交互个性化和治疗效果。
摘要：
认知障碍正成为一项重大的公共健康挑战。认知刺激疗法（CST）是治疗认知障碍的有效干预措施，但传统方法难以扩展，现有数字系统在处理群体对话和认知刺激原则方面存在困难。尽管大语言模型（LLMs）非常强大，但将其应用于此领域面临关键挑战：认知刺激对话范式、缺乏治疗推理以及仅静态的用户建模。为了解决这些问题，我们提出了一种基于原则的自适应策略，通过群体认知刺激对话（GCSD）系统实现。我们首先构建了一个包含超过500小时真实世界CST对话和10000多个通过我们基于原则的场景模拟策略生成的模拟对话的数据集。我们的GCSD系统随后集成了四个核心模块以克服LLM的局限性：（i）多说话者上下文控制器以解决角色混淆；（ii）动态参与者认知状态建模以实现个性化交互；（iii）专注于认知刺激的注意力丧失以培养认知刺激推理；（iv）多维奖励策略以增强响应价值。实验结果表明，GCSD在各种评估指标上均显著优于基线模型。未来的工作将专注于长期临床验证，以弥合计算性能与临床效果之间的差距。
英文摘要：
Cognitive impairment is becoming a major public health challenge. Cognitive Stimulation Therapy (CST) is an effective intervention for cognitive impairment, but traditional methods are difficult to scale, and existing digital systems struggle with group dialogues and cognitive stimulation principles. While Large Language Models (LLMs) are powerful, their application in this context faces key challenges: cognitive stimulation dialogue paradigms, a lack of therapeutic reasoning, and static-only user modeling. To address these issues, we propose a principle-driven adaptive policy actualized through a Group Cognitive Stimulation Dialogue (GCSD) system. We first construct a dataset with over 500 hours of real-world CST conversations and 10,000+ simulated dialogues generated via our Principle-Guided Scenario Simulation strategy. Our GCSD system then integrates four core modules to overcome LLM limitations: (i) a multi-speaker context controller to resolve role confusion; (ii) dynamic participant cognitive state modeling for personalized interaction; (iii) a cognitive stimulation-focused attention loss to instill cognitive stimulation reasoning; and (iv) a multi-dimensional reward strategy to enhance response value. Experimental results demonstrate that GCSD significantly outperforms baseline models across various evaluation metrics. Future work will focus on long-term clinical validation to bridge the gap between computational performance and clinical efficacy.
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