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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Large language models for science and medicine.

Amalio Telenti^1,2, Michael Auli³, Brian L Hie^3,4

¹Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, California, USA.

European Journal of Clinical Investigation

|February 21, 2024

Summary

Large language models (LLMs) are powerful AI tools that analyze text patterns. These advanced models show transformative potential across diverse scientific and medical applications, from analyzing research papers to understanding complex biological data.

Keywords:

applications large language models limitations medicine science validation

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Area of Science:

Artificial Intelligence in Medicine
Computational Biology
Bioinformatics

Background:

Large language models (LLMs) are machine learning models adept at identifying statistical patterns in text.
LLMs, both general and specialized, show promise in various fields.
Science and medicine possess abundant data types suitable for LLM analysis.

Purpose of the Study:

To explore the potential of large language models (LLMs) in science and medicine.
To highlight the suitability of diverse scientific and medical data for LLM applications.
To discuss the transformative impact of carefully validated LLM systems.

Main Methods:

Analysis of text patterns using machine learning.
Application of LLMs to diverse data types including scientific literature, electronic health records, and molecular sequences.
Focus on validation of systems for cross-modal understanding and reasoning.

Main Results:

LLMs demonstrate capability in understanding and processing complex scientific and medical data.
Potential applications span analysis of publications, patents, electronic medical records, DNA/protein sequences, and chemical compounds.
Carefully validated LLM systems are key to maximizing benefits.

Conclusions:

Large language models (LLMs) possess the potential to revolutionize scientific and medical research.
Despite inherent limitations and uncertainties, LLMs offer significant opportunities for advancement.
Cross-modal understanding and reasoning in LLMs are crucial for realizing their full potential in these fields.