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.

翻译：大语言模型（LLMs）正日益支持广泛领域的应用，其中一些领域（如生物医学）具有潜在的重大社会影响，然而这些模型在实际使用场景中的可靠性尚未得到充分研究。本文提出了生物医学LLM助手可靠性评估（RAmBLA）框架，并评估了四种最先进的基础LLM能否作为生物医学领域的可靠助手。我们确定了提示鲁棒性、高召回率和无幻觉作为该用例的必要标准。我们设计了短格式任务和需要LLM自由形式回答的任务，以模拟真实用户交互。通过评估器LLM，我们利用LLM回答与真实回答的语义相似性来评估其性能。