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Although large language models (LLMs) have achieved great success in vast real-world applications, their vulnerabilities towards noisy inputs have significantly limited their uses, especially in high-stake environments.
Achieving verified robustness to symbol substitutions via interval bound propagation
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An efficient and margin-approaching zero-confidence adversarial attack
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Safer: A structure-free approach for certified robustness to adversarial word substitutions
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Fast and precise certification of transformers
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Certified robustness to word substitution attack with differential privacy
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Certified robustness to text adversarial attacks by randomized [mask]
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Certified robustness to text adversarial attacks by randomized [mask]
Jiehang Zeng, Xiaoqing Zheng, Jianhan Xu, Linyang Li, Liping Yuan, and Xuanjing Huang. 2021b
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Certified robustness against natural language attacks by causal intervention
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Bloomberggpt: A large language model for finance
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