Large Language Models (LLMs), such as GPT3.5, have exhibited remarkable proficiency in comprehending and generating natural language. On the other hand, medical assistants hold the potential to offer substantial benefits for individuals. However, the exploration of LLM-based personalized medical assistant remains relatively scarce. Typically, patients converse differently based on their background and preferences which necessitates the task of enhancing user-oriented medical assistant. While one can fully train an LLM for this objective, the resource consumption is unaffordable. Prior research has explored memory-based methods to enhance the response with aware of previous mistakes for new queries during a dialogue session. We contend that a mere memory module is inadequate and fully training an LLM can be excessively costly. In this study, we propose a novel computational bionic memory mechanism, equipped with a parameter-efficient fine-tuning (PEFT) schema, to personalize medical assistants.

翻译：大型语言模型（LLMs），如GPT3.5，在理解和生成自然语言方面展现出卓越能力。另一方面，医疗助手具备为个体提供显著利益的潜力。然而，基于大语言模型的个性化医疗助手研究仍相对匮乏。通常，患者会根据自身背景和偏好以不同方式交流，这要求我们强化面向用户的医疗助手任务。尽管可以针对此目标完全训练大语言模型，但资源消耗难以承受。先前研究探索了基于记忆的方法，通过记住对话过程中的先前错误来增强对新查询的回应。我们认为，仅靠记忆模块是不够的，而完全训练大语言模型成本过高。在本研究中，我们提出一种新颖的计算仿生记忆机制，并配备参数高效微调（PEFT）架构，以实现医疗助手的个性化。