NLP Verification: Towards a General Methodology for Certifying Robustness

arXiv:2403.10144v1 Announce Type: cross
Abstract: Deep neural networks have exhibited substantial success in the field of Natural Language Processing (NLP) and ensuring their safety and reliability is crucial: there are safety critical contexts where such models must be robust to variability or attack, and give guarantees over their output. Unlike Computer Vision, NLP lacks a unified verification methodology and, despite recent advancements in literature, they are often light on the pragmatical issues of NLP verification. In this paper, we make …

abstract arxiv computer cs.ai cs.cl cs.lg cs.lo cs.pl general language language processing methodology natural natural language natural language processing networks neural networks nlp processing reliability robust robustness safety success type verification