  This paper provides a thorough examination of recent developments in the
field of multi-choice Machine Reading Comprehension (MRC). Focused on benchmark
datasets, methodologies, challenges, and future trajectories, our goal is to
offer researchers a comprehensive overview of the current landscape in
multi-choice MRC. The analysis delves into 30 existing cloze-style and
multiple-choice MRC benchmark datasets, employing a refined classification
method based on attributes such as corpus style, domain, complexity, context
style, question style, and answer style. This classification system enhances
our understanding of each dataset's diverse attributes and categorizes them
based on their complexity. Furthermore, the paper categorizes recent
methodologies into Fine-tuned and Prompt-tuned methods. Fine-tuned methods
involve adapting pre-trained language models (PLMs) to a specific task through
retraining on domain-specific datasets, while prompt-tuned methods use prompts
to guide PLM response generation, presenting potential applications in
zero-shot or few-shot learning scenarios. By contributing to ongoing
discussions, inspiring future research directions, and fostering innovations,
this paper aims to propel multi-choice MRC towards new frontiers of
achievement.
