GenQA Model

GenQA models represent a significant advancement in question answering (QA) systems, moving beyond simply extracting answers to generating them from multiple retrieved text segments. Current research focuses on improving GenQA training, particularly through techniques that leverage knowledge from existing answer ranking models and incorporate automatic evaluation metrics to enhance accuracy. This approach addresses the challenge of creating large, labeled datasets for training these generative models, ultimately leading to more accurate and natural-sounding answers in various QA applications.