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Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT

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arxiv 2003.04985 v1 pith:6QAPZG3W submitted 2020-02-27 cs.CL

Lichao Sun , Kazuma Hashimoto , Wenpeng Yin , Akari Asai , Jia Li , Philip Yu , Caiming Xiong This is my paper
classification cs.CL
keywords adversarialbertdealingtyposwordsadv-bertamountanalysis
verification ladder T0 review T1 audit T2 compute T3 formal
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There is an increasing amount of literature that claims the brittleness of deep neural networks in dealing with adversarial examples that are created maliciously. It is unclear, however, how the models will perform in realistic scenarios where \textit{natural rather than malicious} adversarial instances often exist. This work systematically explores the robustness of BERT, the state-of-the-art Transformer-style model in NLP, in dealing with noisy data, particularly mistakes in typing the keyboard, that occur inadvertently. Intensive experiments on sentiment analysis and question answering benchmarks indicate that: (i) Typos in various words of a sentence do not influence equally. The typos in informative words make severer damages; (ii) Mistype is the most damaging factor, compared with inserting, deleting, etc.; (iii) Humans and machines have different focuses on recognizing adversarial attacks.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  2. SALMAN: Stability Analysis of Language Models Through the Maps Between Graph-based Manifolds

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    SALMAN ranks each text sample's fragility via the distortion between input and output embedding distances and uses the ranking to improve attack success rates and fine-tuning robustness.

  3. Handling Korean Out-of-Vocabulary Words with Phoneme Representation Learning

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    KOPL combines phoneme and word representations to improve Korean out-of-vocabulary word embeddings, outperforming prior methods by 1.9 percent on average across five tasks.

  4. Coordinated Robustness Evaluation Framework for Vision-Language Models

    cs.CV 2025-06 conditional novelty 4.0 of 10

    A coordinated image-plus-text attack built on a surrogate multimodal encoder achieves 80-94% attack success against ViLT, BLIP, and GIT on VQA and visual reasoning, surpassing cited baselines.

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