Long-Term Memory Mechanism of Large Language Models for Personalized Medical Inquiry Service
TL;DR
A dynamic memory retrieval framework based on dual-graph enhancement that constructs a two-tier architecture of fine-grained event graph and macro portrait to maintain the evolution trend summary of the patient’s condition, psychology and living habits.
Abstract
The Large Language Model (LLM) provides a more efficient and convenient way for long-term medical dialogue by using its understanding and generation ability. However, LLM also has some core challenges, such as the forgetting of key medical history and the insufficient capture of disease trend. This paper proposes a dynamic memory retrieval framework based on dual-graph enhancement. The framework constructs a two-tier architecture of fine-grained event graph and macro portrait. Specifically, the event graph connects events across time through entity nodes, and saves the semantic relationships between events; The portrait part organizes the events in multiple macro dimensions to maintain the evolution trend summary of the patient’s condition, psychology and living habits. Experiments based on the Long-Term Health Monitoring Dataset (LTHM) show that the comprehensiveness of this framework is 0.708, which is superior to other baseline models, with the relevance of 0.973 and the faithfulness of 0.947, ensuring the accuracy and reliability of the answers.