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
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.