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LLM-based Personalized Medical Assistant with Dual-Process Enhanced Memory and Parameter-Efficient Fine-Tuning


Core Concepts
A novel computational bionic memory mechanism, equipped with a parameter-efficient fine-tuning schema, is proposed to personalize medical assistants and enhance their response quality by catering to user-specific needs.
Abstract
The content discusses the development of an LLM-based personalized medical assistant that leverages a novel Dual-Process enhanced Memory (DPeM) mechanism and a Parameter-Efficient Fine-Tuning (PEFT) approach. Key highlights: Existing memory-based methods for enhancing LLM responses are limited by their inflexible dictionary-based structure and inability to provide personalized experiences. The proposed DPeM mechanism is inspired by the real-world memory processes, comprising working memory, short-term memory, and long-term memory, which cooperate under a dual-process schema to provide more useful knowledge from both user-specific and common-sense perspectives. The PEFT approach is employed to fine-tune the LLM in a user-friendly manner, reducing computational and data resource requirements compared to fully training personalized LLMs. A new medical dialogue dataset is introduced, which incorporates user preferences and historical records to explore personalized medical assistants. Extensive experiments and human evaluation demonstrate the effectiveness of the proposed MaLP framework, which integrates DPeM and PEFT, in enhancing the quality of personalized medical assistant responses.
Stats
LLMs, such as GPT3.5, have exhibited remarkable proficiency in comprehending and generating natural language. Medical assistants hold the potential to offer substantial benefits for individuals, but the exploration of LLM-based personalized medical assistants remains relatively scarce. Patients converse differently based on their background and preferences, necessitating the task of enhancing user-oriented medical assistants.
Quotes
"Typically, patients converse differently based on their background and preferences which necessitates the task of enhancing user-oriented medical assistant." "We contend that a mere memory module is inadequate and fully training an LLM can be excessively costly."

Deeper Inquiries

How can the proposed DPeM mechanism be further extended to handle more complex memory processes, such as avoidance learning?

The DPeM mechanism, inspired by neuroscience's dual-process theory, can be extended to handle more complex memory processes like avoidance learning by incorporating additional learning schemas or losses. To address avoidance learning, where certain information needs to be avoided or corrected, the DPeM mechanism can be enhanced with specialized modules that can identify and regulate avoidance behaviors. By introducing learning schemas that can detect and prevent the retrieval of incorrect or harmful information, the DPeM mechanism can adapt to handle avoidance learning scenarios effectively. This extension would involve refining the memory structure to include mechanisms for identifying and filtering out undesirable information, ensuring that the LLM generates accurate and safe responses.

What are the potential privacy concerns and technical challenges in scaling the personalized medical assistant approach to millions of users, and how can they be addressed?

Scaling the personalized medical assistant approach to millions of users poses several potential privacy concerns and technical challenges. Privacy concerns may arise from the storage and utilization of sensitive medical information, user preferences, and historical dialogue data. Ensuring data security, compliance with privacy regulations, and implementing robust encryption and access control measures are essential to address these concerns. Technical challenges include the computational resources required to support millions of users, the efficient retrieval and processing of personalized information, and maintaining the quality and accuracy of responses at scale. To address these challenges, a distributed computing infrastructure can be implemented to handle the increased workload efficiently. Federated learning techniques can be utilized to train models across multiple devices while preserving data privacy. Implementing data anonymization techniques, secure data transmission protocols, and regular security audits can help mitigate privacy risks. Additionally, employing advanced data management strategies, such as data partitioning and caching, can optimize the performance of the personalized medical assistant system for a large user base.

How can the integration of the DPeM mechanism and PEFT approach be leveraged to enhance personalization in other domains beyond medical assistants, such as educational or entertainment applications?

The integration of the DPeM mechanism and PEFT approach can be leveraged to enhance personalization in various domains beyond medical assistants, such as educational or entertainment applications. In educational settings, the DPeM mechanism can be used to tailor learning materials based on individual student preferences and learning styles. By incorporating user-specific knowledge and feedback, the system can provide personalized recommendations, feedback, and explanations to enhance the learning experience. In entertainment applications, the DPeM mechanism can be utilized to create personalized content recommendations, interactive storytelling experiences, and tailored responses in chatbots or virtual assistants. By understanding user preferences, historical interactions, and context, the system can generate engaging and customized content that resonates with each user. Overall, the integration of the DPeM mechanism and PEFT approach can revolutionize personalization in various domains by enabling adaptive and responsive systems that cater to individual needs and preferences. This approach can lead to more engaging, effective, and user-centric experiences across a wide range of applications.
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