Multiple Choice Question

Multiple-choice questions (MCQs) are widely used to evaluate large language models (LLMs), aiming to assess their knowledge, reasoning, and critical thinking abilities across diverse domains. Current research focuses on improving LLM performance on MCQs, exploring techniques like retrieval-augmented generation, fine-tuning with tailored demonstrations, and mitigating biases such as positional preferences and over-reliance on answer choices. This research is significant because robust and unbiased MCQ benchmarks are crucial for evaluating LLM capabilities and ensuring their reliable application in education, professional certification, and other high-stakes contexts.