Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering

Robustness in Natural Language Processing continues to be a pertinent issue, where state of the art models under-perform under naturally shifted distributions. In the context of Question Answering, work on domain adaptation methods continues to be a growing body of research. However, very little attention has been given to the notion of domain generalization under natural distribution shifts, where the target domain is unknown. With drastic improvements in the quality and access to generative models, we answer the question: How …

art attention augmentation context cs.ai cs.cl data domain domain adaptation generative issue language language processing llms natural natural language natural language processing processing question question answering research robustness state state of the art work