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arXiv · 2403.14578

RAmBLA: A Framework for Evaluating the Reliability of LLMs as Assistants in the Biomedical Domain

William James Bolton·Rafael Poyiadzi·Edward R. Morrell·Gabriela van Bergen Gonzalez Bueno·Lea Goetz
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Abstract

Large Language Models (LLMs) increasingly support applications in a wide range of domains, some with potential high societal impact such as biomedicine, yet their reliability in realistic use cases is under-researched. In this work we introduce the Reliability AssesMent for Biomedical LLM Assistants (RAmBLA) framework and evaluate whether four state-of-the-art foundation LLMs can serve as reliable assistants in the biomedical domain. We identify prompt robustness, high recall, and a lack of hallucinations as necessary criteria for this use case. We design shortform tasks and tasks requiring LLM freeform responses mimicking real-world user interactions. We evaluate LLM performance using semantic similarity with a ground truth response, through an evaluator LLM.

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William James Bolton, Rafael Poyiadzi, Edward R. Morrell, Gabriela van Bergen Gonzalez Bueno, Lea Goetz. 2024-03-21. RAmBLA: A Framework for Evaluating the Reliability of LLMs as Assistants in the Biomedical Domain. https://arxiv.org/abs/2403.14578

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