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A Principle-Driven Adaptive Policy for Group Cognitive Stimulation Dialogue for Elderly with Cognitive Impairment

Jiyue Jiang, Yanyu Chen, Pengan Chen, Kai Liu, Jingqi Zhou, Zheyong Zhu, He Hu, Fei Ma, Qi Tian, Chuan Wu

TL;DR

This work proposes a principle-driven adaptive policy actualized through a Group Cognitive Stimulation Dialogue (GCSD) system and demonstrates that GCSD significantly outperforms baseline models across various evaluation metrics.

Abstract

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.

A Principle-Driven Adaptive Policy for Group Cognitive Stimulation Dialogue for Elderly with Cognitive Impairment

TL;DR

This work proposes a principle-driven adaptive policy actualized through a Group Cognitive Stimulation Dialogue (GCSD) system and demonstrates that GCSD significantly outperforms baseline models across various evaluation metrics.

Abstract

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.
Paper Structure (26 sections, 6 equations, 2 figures, 3 tables, 1 algorithm)

This paper contains 26 sections, 6 equations, 2 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Cognitive Training Dialogue Systems
  4. Data Construction
  5. Real Data Construction
  6. Simulated Data Construction - Principle-Guided Scenario Simulation Strategy
  7. Methodology: GCSD
  8. Overview
  9. Multi-Party Context Control
  10. Dynamic Participant Cognitive State Modeling
  11. Soft Prompt Generation Network.
  12. Integration and Smoothing.
  13. Optimization Strategy
  14. Phase 1: Supervised Fine-Tuning with Cognitive Stimulation-Focused Attention.
  15. Phase 2: Multi-Reward Policy Optimization (MRPO).
  16. ...and 11 more sections

Figures (2)

  • Figure 1: The construction of real and simulated datasets.
  • Figure 2: This is an overview of the GCSD. On the left side is the data construction phase, which includes the workflows for both real data and simulated data construction (principle-guided scenario simulation strategy), as well as an example shown at the lower left. On the right side is the GCSD model phase, where the model first learns patterns and scenarios based on simulated data, and then trains on real data. The model consists of four parts: multi-speaker context controller, dynamic participant cognitive state modeling, cognitive stimulation-focused attention Loss, and multi-reward policy optimization.