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Related Experiment Video

Updated: Jun 27, 2025

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

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

Published on: December 6, 2024

Large language models leverage external knowledge to extend clinical insight beyond language boundaries.

Jiageng Wu¹, Xian Wu², Zhaopeng Qiu²

¹School of Public Health, Zhejiang University School of Medicine, Hangzhou, 310058, China.

Journal of the American Medical Informatics Association : JAMIA

|April 29, 2024

Summary

This summary is machine-generated.

This study introduces a novel framework to improve Large Language Models (LLMs) for non-English medical exams. The Knowledge and Few-shot Enhancement In-context Learning (KFE) framework significantly boosts LLM performance, enabling them to pass medical licensing examinations.

Keywords:

clinical knowledge large language models medical examination natural language processing

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Area of Science:

Artificial Intelligence in Medicine
Natural Language Processing
Medical Education Technology

Background:

Large Language Models (LLMs) show promise in medical question-answering but struggle in non-English contexts due to imbalanced training data.
English-centric LLMs face challenges in diverse clinical settings, limiting their global applicability and potentially exacerbating healthcare disparities.

Purpose of the Study:

To systematically evaluate LLMs in the Chinese medical context using the China National Medical Licensing Examination (CNMLE-2022).
To develop and assess a novel in-context learning framework, Knowledge and Few-shot Enhancement In-context Learning (KFE), to enhance LLM performance in non-English medical scenarios.

Main Methods:

Constructed a comprehensive medical knowledge base and question bank from 53 medical books and 381,149 questions for CNMLE-2022.
Implemented the KFE framework to integrate diverse external clinical knowledge sources into LLMs via in-context learning.
Evaluated KFE with multiple LLMs (ChatGPT, GPT-4, Baichuan2, QWEN) on CNMLE-2022, analyzing performance across different knowledge integration pathways.

Main Results:

Direct application of ChatGPT on CNMLE-2022 yielded a score of 51, below the passing threshold.
The KFE framework significantly improved LLM performance: ChatGPT reached 70.04, and GPT-4 achieved 82.59, exceeding the average human score (68.70).
A smaller model, Baichuan2-13B, successfully passed the examination using KFE, demonstrating potential for low-resource settings.

Conclusions:

The KFE framework effectively enhances LLM capabilities in non-English medical question-answering tasks.
Synergizing medical knowledge through in-context learning overcomes language barriers, extending clinical insight and reducing disparities in LLM applications.
This approach ensures broader global benefit from LLM advancements in healthcare.