Robustness in Natural Language Processing: Addressing Challenges in Text-based AI Systems
K. Rajchandar, Geetha Manoharan, Sunitha Purushottam Ashtikar · 2024
Though natural language processing (NLP) has developed prototype models that can handle a range of language events and navigate through adversarial situations, the discipline has made significant progress in the last few years in several linguistic tasks. However, the robustness of AI mechanisms that parse text has been a serious source of concern. This study makes an effort to address the issues that crop up with NLP methodologies. Examining these study limits will enable us to pinpoint areas where the existing NLP systems need to be improved. Currently, the designs and training paradigms of the NLP models in use are thoroughly scrutinized and reviewed.