Medical Multi-Choice Question Answering with Retrieval Augmented Language Models

Medical multi-choice question-answering (Medical MCQA) is an emerging topic with great practical importance for diagnosis and treatment. However, this task is under-explored due to the scarcity of data, whose annotation cost is relatively high, demanding domain-specific knowledge. Some methods attempt to expand data scale through mitigation strategies based on large-scale unsupervised corpora. Despite their promising results, pre-trained language models, lacking medical knowledge, fail to make accurate predictions for professional questions about medicine. To address this, we applied the Retrieval-Augmented method to Medical MCQA and proposed a new framework based on interactive retrieval augmentation. This framework consists of two parts for adapting knowledge to the medical domain. Firstly, the dynamic interplay between inquiries and contextual nuances is learned in the data-rich domain through QA modeling of language models. This procedural knowledge and these implicit semantic associations embedded in vocabulary representations are adapted to the medical domain for better understanding. Secondly, an adaptive retrieval network is designed to inject knowledge into the model. Our method has shown superior performance on multiple Medical MCQA datasets compared to baseline models, effectively addressing the challenges posed by data scarcity and domain specialization of Medical MCQA. Analysis showcases that our model has a better ability to retrieve and comprehend medical details.