Machine Reading Comprehension

Machine reading comprehension (MRC) focuses on enabling computers to understand and answer questions based on given text, aiming to bridge the gap between human and machine understanding of language. Current research emphasizes improving the robustness and efficiency of MRC models, particularly through the use of large language models (LLMs), retrieval-augmented methods, and innovative techniques like prompt engineering and multi-level prompt tuning to address challenges such as handling multi-document contexts, noisy data, and diverse question types. The advancements in MRC have significant implications for various fields, including information retrieval, question answering systems, and knowledge extraction from diverse sources like medical records and historical documents.