Inferring over and extracting information from Large Language Models (LLMs) trained on a large corpus of scientific literature can potentially drive a new era in biomedical research, reducing the barriers for accessing existing medical evidence. This work examines the potential of LLMs for dialoguing with biomedical background knowledge, using the context of antibiotic discovery as an exemplar motivational scenario. The context of biomedical discovery from natural products entails understanding the relational evidence between an organism, an associated chemical and its associated antibiotic properties. We provide a systematic assessment on the ability of LLMs to encode and express these relations, verifying for fluency, prompt-alignment, semantic coherence, factual knowledge and specificity of generated responses. The systematic analysis is applied to nine state-of-the-art models (including ChatGPT and GPT-4) in two prompting-based tasks: chemical compound definition generation and chemical compound-fungus relation determination. Results show that while recent models have improved in fluency, factual accuracy is still low and models are biased towards over-represented entities. The ability of LLMs to serve as biomedical knowledge bases is questioned, and the need for additional systematic evaluation frameworks is highlighted. The best performing GPT-4 produced a factual definition for 70% of chemical compounds and 43.6% factual relations to fungi, whereas the best open source model BioGPT-large 30% of the compounds and 30% of the relations for the best-performing prompt. The results show that while LLMs are currently not fit for purpose to be used as biomedical factual knowledge bases, there is a promising emerging property in the direction of factuality as the models become domain specialised, scale-up in size and level of human feedback.

翻译：从大规模科学文献语料训练的大型语言模型（LLMs）中推理和提取信息，有望推动生物医学研究进入新时代，降低获取现有医学证据的障碍。本研究以抗生素发现为范例动机场景，考察LLMs与生物医学背景知识对话的潜力。天然产物生物医学发现需要理解生物体、相关化学物质及其抗生素属性之间的关联证据。我们系统评估了LLMs编码和表达这些关系的能力，验证了生成响应的流畅性、提示对齐度、语义连贯性、事实知识和特异性。该系统性分析应用于九种最先进模型（包括ChatGPT和GPT-4），涵盖两项基于提示的任务：化合物定义生成和化合物-真菌关系判定。结果表明，尽管近期模型在流畅性方面有所提升，但事实准确性仍然较低，且模型偏向于过度表征的实体。本文质疑了LLMs作为生物医学知识库的能力，并强调需要额外的系统性评估框架。性能最佳的GPT-4对70%的化合物生成了事实性定义，对43.6%的化合物-真菌关系给出了事实性判定；而最佳开源模型BioGPT-large在最优提示下对30%的化合物和30%的关系实现了事实性输出。结果表明，尽管LLMs目前尚不适合用作生物医学事实知识库，但随着模型在领域专业化、规模扩大和人类反馈水平提升方面的发展，在事实性方向上展现出有希望的新兴特性。