Large Language Models for Multi-Choice Question Classification of
  Medical Subjects
Víctor Ponce-López
The aim of this paper is to evaluate whether large language models trained on
multi-choice question data can be used to discriminate between medical
subjects. This is an important and challenging task for automatic question
answering. To achieve this goal, we train deep neural networks for multi-class
classification of questions into the inferred medical subjects. Using our
Multi-Question (MQ) Sequence-BERT method, we outperform the state-of-the-art
results on the MedMCQA dataset with an accuracy of 0.68 and 0.60 on their
development and test sets, respectively. In this sense, we show the capability
of AI and LLMs in particular for multi-classification tasks in the Healthcare
domain.