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Language Models (LMs) have performed well on biomedical natural languageprocessing applications. In this study, we conducted some experiments to useprompt methods to extract knowledge from LMs as new knowledge Bases (LMs asKBs). However, prompting can only be used as a low bound for knowledgeextraction, and perform particularly poorly on biomedical domain KBs. In orderto make LMs as KBs more in line with the actual application scenarios of thebiomedical domain, we specifically add EHR notes as context to the prompt toimprove the low bound in the biomedical domain. We design and validate a seriesof experiments for our Dynamic-Context-BioLAMA task. Our experiments show thatthe knowledge possessed by those language models can distinguish the correctknowledge from the noise knowledge in the EHR notes, and such distinguishingability can also be used as a new metric to evaluate the amount of knowledgepossessed by the model.

Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context