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Updated: Sep 10, 2025
03:14
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
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Development of a context-aware integrated training module based on large language models for continuous education in
Anna Romanyukha1, Mahta Mazloumi1, Thomas De Waelheyns1
1Qaelum NV, Leuven, Belgium.
Summary
A new AI model offers reliable, quick access to radiation protection information for healthcare professionals. This domain-specific large language model (LLM) uses controlled knowledge for continuous education, improving research and training.
Area of Science:
- Artificial Intelligence in Healthcare
- Radiation Protection Science
- Medical Education Technology
Background:
- Large language models (LLMs) require domain-specific tuning for reliable information delivery.
- Healthcare education and research demand accurate, easily accessible scientific knowledge.
- Continuous education in radiation protection is crucial for professionals and researchers.
Purpose of the Study:
- To develop a domain-specific LLM for continuous education in radiation protection.
- To enable users to query specific topics and receive reliable scientific information.
- To reduce the need for manual review of extensive educational materials.
Main Methods:
- Developed and trained a domain-specific LLM using custom radiation protection knowledge.
- Tested the model with various scenarios and fine-tuned hyperparameters (e.g., top k, chunk size, temperature).
- Evaluated model performance based on accuracy and reliability of responses.
Main Results:
- The LLM provided reliable and accurate answers to diverse user queries on radiation protection topics.
- Hyperparameter tuning, particularly embedding model and similarity cutoff, significantly impacted performance.
- The model successfully processed and delivered information from controlled educational materials.
Conclusions:
- The validated LLM effectively supports continuous education in radiation protection.
- Users can quickly and reliably access information on radiobiology and radiation safety.
- This AI tool enhances learning for healthcare professionals and researchers in the field.
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