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

• URL: http://arxiv.org/abs/2403.14578v1
• Date: Thu, 21 Mar 2024 17:30:59 GMT
• Title: RAmBLA: A Framework for Evaluating the Reliability of LLMs as Assistants in the Biomedical Domain
• Abstract summary: We introduce the Reliability AssesMent for Biomedical LLM Assistants framework. We evaluate whether four state-of-the-art foundation LLMs can serve as reliable assistants in the biomedical domain.
• Score: 0.37282630026096586
• License: http://creativecommons.org/licenses/by/4.0/
• 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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