MedREQAL: Examining Medical Knowledge Recall of Large Language Models via Question Answering

In recent years, Large Language Models (LLMs) have demonstrated an impressive ability to encode knowledge during pre-training on large text corpora. They can leverage this knowledge for downstream tasks like question answering (QA), even in complex areas involving health topics. Considering their high potential for facilitating clinical work in the future, understanding the quality of encoded medical knowledge and its recall in LLMs is an important step forward. In this study, we examine the capability of LLMs to exhibit medical knowledge recall by constructing a novel dataset derived from systematic reviews -studies synthesizing evidence-based answers for specific medical questions. Through experiments on the new MedREQAL dataset, comprising questionanswer pairs extracted from rigorous systematic reviews, we assess six LLMs, such as GPT and Mixtral, analyzing their classification and generation performance. Our experimental insights into LLM performance on the novel biomedical QA dataset reveal the still challenging nature of this task. Objective: To assess the effects of listening to music on sleep in adults with insomnia and to assess the influence of specific variables that may moderate the effect. Generated question: Can listening to music improve sleep in adults with insomnia? Conclusion: The findings of this review provide evidence that music may be effective for improving subjective sleep quality in adults with symptoms of insomnia. (...