Universal Adversarial Triggers for Attacking and Analyzing NLP
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Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
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Pre-trained embeddings and LMs can perform simple operations such as addition and subtraction when the number of digits is small but fail when the numbers are larger.
“Within the context of pre-trained LMs, researchers have found that pre-trained embeddings and LMs can perform simple operations such as addition and subtraction when the number of digits is small but fail when the numbers are larger [9,88,139].”
From Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (2023) · cites this paper as [139]
The Sentiment Analysis and Natural Language Inference sections discuss models using pre-trained embeddings, but report task accuracy rather than arithmetic ability. Searches for addition, subtraction, and number of digits found no passage addressing whether performance changes with number size. (LLM, conf=0.76)
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“Recent work also explores ways of using LLMs to generate “red-teaming” data (Perez et al., 2022; Ganguli et al., 2022), and others uses gradient-based transfer attacks to produce even stronger adversaries (Wallace et al., 2019; Zou et al., 2023; Geiping et al., 2024).”
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- Authors
- Wallace, E., Feng, S., Kandpal, N., Gardner, M., & Singh, S.
- Affiliations
- Allen Institute for Artificial Intelligence (US)
- University of Maryland, College Park (US)
- University of California, Irvine (US)
- Venue
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Published
- 2019
- Pages
- 2153-2162
- OpenAlex
- W2970290563
- Cited by
- 642
- References
- 32
- Citations by year
- 2026: 50 · 2025: 68 · 2024: 90 · 2023: 96 · 2022: 100 · 2021: 151 · 2020: 83 · 2019: 4
- FWCI
- 31.63
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1822494, 1756023)
- Raytheon Company
- Defense Advanced Research Projects Agency (HR0011-15-C-0113, HR0011)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- adversarial triggers, adversarial attacks, natural language processing, robustness, adversarial examples, text classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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