Article
Version 1
Preserved in Portico This version is not peer-reviewed
LLM- Based Personalized Recommendations in Health
Version 1
: Received: 28 February 2024 / Approved: 29 February 2024 / Online: 29 February 2024 (12:51:47 CET)
How to cite: Galitsky, B.A. LLM- Based Personalized Recommendations in Health. Preprints 2024, 2024021709. https://doi.org/10.20944/preprints202402.1709.v1 Galitsky, B.A. LLM- Based Personalized Recommendations in Health. Preprints 2024, 2024021709. https://doi.org/10.20944/preprints202402.1709.v1
Abstract
Today, LLMs are not good at personalization providing recommendation. They advise physicians and financial advisors to ask professionals in respective fields for help, even having user information available. Answering questions of software professionals, LLM needs to deliver in-depth answers with codes or algorithms, whereas for professionals in other fields would need definitions and main concepts. The intent of this chapter is to make LLM answer tailored to the needs of users, taking into account available information about them. To do that, we need to generalize available information about a person like her health record, maintaining the privacy of this person. We rely on meta-learning techniques to design a LLM prompt to produce a personalization prompt to obtain a suitable relevant information. Such “meta-prompt” is produced by generalization operation applied to available documents for the user. These documents need to be de-identified so that they are sufficient for personalization on one hand and will maintain user privacy on the other hand. The second neuro-symbolic technique to support personalization is abductive reasoning, acting in parallel to LLM fine-tuning. Traditional recommendation and personalization techniques as well as modern, deep learning – based are presented, and the comparison is drawn to the proposed approach. We also share the evaluation and comparative analyses of these approaches. We consider an example for how to build personalization LLM systems coming from Langchain platform. We will explore how to construct chains to form a personalization profile for a user and apply it to user search and recommendation requests.
Keywords
Large Language Models; personalization; recommendation; meta-prompts; abductive reasoning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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