LLM-based Medical Assistant Personalization with Short- and Long-Term Memory Coordination

Kai Zhang, Yangyang Kang, Fubang Zhao, Xiaozhong Liu


Abstract
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. To encourage further research into this area, we are releasing a new conversation dataset generated based on an open-source medical corpus and our implementation.
Anthology ID:
2024.naacl-long.132
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2386–2398
Language:
URL:
https://aclanthology.org/2024.naacl-long.132
DOI:
Bibkey:
Cite (ACL):
Kai Zhang, Yangyang Kang, Fubang Zhao, and Xiaozhong Liu. 2024. LLM-based Medical Assistant Personalization with Short- and Long-Term Memory Coordination. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 2386–2398, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
LLM-based Medical Assistant Personalization with Short- and Long-Term Memory Coordination (Zhang et al., NAACL 2024)
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https://aclanthology.org/2024.naacl-long.132.pdf
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