  To address the challenges of out-of-control generation in generative models
for machine reading comprehension (MRC), we introduce the Question-Attended
Span Extraction (QASE) module. Integrated during the fine-tuning of pre-trained
generative language models (PLMs), QASE enables these PLMs to match SOTA
extractive methods and outperform leading LLMs like GPT-4 in MRC tasks, without
significant increases in computational costs.
