Multiple Choice Question Answering

Multiple-choice question answering (MCQA) focuses on developing computational models that can accurately select the correct answer from a set of options given a question. Current research emphasizes improving model performance by leveraging the relationships between answer choices, exploring different model architectures such as transformers and binary classification approaches, and developing more robust evaluation methods beyond simple accuracy metrics. This field is significant for advancing natural language understanding and has practical applications in education, healthcare (e.g., medical licensing exams), and other domains requiring automated question-answering systems.