How does the effectiveness of negative sampling for unanswerable questions in the MRQA dataset compare to SQuA

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More i

Research goal: How does the effectiveness of negative sampling for unanswerable questions in the MRQA dataset compare to SQuAD 2.0 in terms of F1 and exact match scores, and what is the impact on inference latency when using this sampling strategy versus random sampling across different transformer architectures?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.7/10.