  There is vivid research on adapting Large Language Models (LLMs) to perform a
variety of tasks in high-stakes domains such as healthcare. Despite their
popularity, there is a lack of understanding of the extent and contributing
factors that allow LLMs to recall relevant knowledge and combine it with
presented information in the clinical and biomedical domain: a fundamental
pre-requisite for success on down-stream tasks. Addressing this gap, we use
Multiple Choice and Abstractive Question Answering to conduct a large-scale
empirical study on 22 datasets in three generalist and three specialist
biomedical sub-domains. Our multifaceted analysis of the performance of 15
LLMs, further broken down by sub-domain, source of knowledge and model
architecture, uncovers success factors such as instruction tuning that lead to
improved recall and comprehension. We further show that while recently proposed
domain-adapted models may lack adequate knowledge, directly fine-tuning on our
collected medical knowledge datasets shows encouraging results, even
generalising to unseen specialist sub-domains. We complement the quantitative
results with a skill-oriented manual error analysis, which reveals a
significant gap between the models' capabilities to simply recall necessary
knowledge and to integrate it with the presented context. To foster research
and collaboration in this field we share M-QALM, our resources, standardised
methodology, and evaluation results, with the research community to facilitate
further advancements in clinical knowledge representation learning within
language models.
