Scalable evaluation framework for retrieval augmented generation in tobacco research using large Language models

Scalable evaluation framework for retrieval augmented generation in tobacco research using large Language models

Abstract Retrieval-augmented generation (RAG) systems show promise in specialized knowledge domains, but the tobacco research field lacks standardized assessment frameworks for comparing different large language models (LLMs). This gap impacts public health decisions that require accurate, domain-sp...

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Bibliographic Details
Main Authors: Sherif Elmitwalli, John Mehegan, Sophie Braznell, Allen Gallagher
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Retrieval-Augmented generation
Domain-Specific information retrieval
AI evaluation
Goal-Question-Metric framework
Large Language models
Expert validation
Online Access:https://doi.org/10.1038/s41598-025-05726-2
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https://doi.org/10.1038/s41598-025-05726-2

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