Papers with AI-enabled model confusion

1 papers
TextVerifier: Robustness Verification for Textual Classifiers with Certifiable Guarantees (2023.findings-acl)

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Challenge: a textual classifier must withstand word-level alteration attacks due to inherent vulnerability.
Approach: They propose a formal verification framework with certifiable guarantees on deep neural networks in natural language processing against word-level alteration attacks.
Outcome: The proposed framework provides an approximation of the maximal safe radius with tight bounds . it yields an efficient speed edge and reliable anytime estimation .

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