Fetching the paper…
Reading the bibliography…
Transformer-based text classifiers such as BERT, RoBERTa, T5, and GPT have shown strong performance in natural language processing tasks but remain vulnerable to adversarial examples.
G. A. Miller, “Wordnet: a lexical database for english,” Communications of the ACM , vol. 38, no. 11, pp. 39–41, 1995
1995
Earlier work this paper cites.
P. Merlo and S. Stevenson, “Automatic verb classification based on statistical distributions of argument structure,” Computational Linguistics , vol. 27, no. 3, pp. 373–408, 2001
2001
Earlier work this paper cites.
J. Ebrahimi, A. Rao, D. Lowd, and D. Dou, “HotFlip: White-box adversarial examples for text classification,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . Melbourne, Australia: Association for Computational Linguistics, Jul. 2018, pp. 31–36. [Online]. Available: https://www.aclweb.org/anthology/P18-2006
2006
Earlier work this paper cites.
S. Bird, “Nltk: the natural language toolkit,” in Proceedings of the COLING/ACL 2006 Interactive Presentation Sessions , 2006, pp. 69–72
2006
Earlier work this paper cites.
A. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning word vectors for sentiment analysis,” in Proceedings of the Annual Meeting of the Association for Computational Linguistics: Human Language Technologies , 2011, pp. 142–150
2011
Earlier work this paper cites.
J. Snoek, H. Larochelle, and R. P. Adams, “Practical bayesian optimization of machine learning algorithms,” Advances in neural information processing systems , vol. 25, pp. 2951–2959, 2012
2012
Earlier work this paper cites.
J. Bergstra, D. Yamins, and D. Cox, “Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures,” in International Conference on Machine Learning . PMLR, 2013, pp. 115–123
2013
Earlier work this paper cites.
Z. Wang, M. Zoghi, F. Hutter, D. Matheson, and N. De Freitas, “Bayesian optimization in high dimensions via random embeddings,” in Twenty-Third international joint conference on artificial intelligence , 2013
2013
Earlier work this paper cites.
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing , 2013, pp. 1631–1642
2013
Earlier work this paper cites.
J. Pennington, R. Socher, and C. D. Manning, “Glove: Global vectors for word representation,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2014, pp. 1532–1543
2014
Earlier work this paper cites.
X. Zhang, J. Zhao, and Y. LeCun, “Character-level convolutional networks for text classification,” Advances in Neural Information Processing Systems , vol. 28, 2015
2015
Earlier work this paper cites.
M. T. Ribeiro, S. Singh, and C. Guestrin, “” why should i trust you?” explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2016, pp. 1135–1144
2016
Earlier work this paper cites.
E. Liberty, K. Lang, and K. Shmakov, “Stratified Sampling Meets Machine Learning,” in Proceedings of The 33rd International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, M. F. Balcan and K. Q. Weinberger, Eds., vol. 48. New York, New York, USA: PMLR, 2016, pp. 2320–2329. [Online]. Available: http://proceedings.mlr.press/v48/liberty16.html
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in Neural Information Processing Systems , vol. 30, 2017
2017
Earlier work this paper cites.
S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” Advances in Neural Information Processing Systems , vol. 30, 2017
2017
Earlier work this paper cites.
K. W. Church, “Word2vec,” Natural Language Engineering , vol. 23, no. 1, pp. 155–162, 2017
2017
Earlier work this paper cites.
P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, “Enriching word vectors with subword information,” Transactions of the Association for Computational Linguistics , vol. 5, pp. 135–146, 2017
2017
Earlier work this paper cites.
J. Gao, J. Lanchantin, M. L. Soffa, and Y. Qi, “Black-box generation of adversarial text sequences to evade deep learning classifiers,” in 2018 IEEE Security and Privacy Workshops (SPW) . IEEE, 2018, pp. 50–56
2018
Earlier work this paper cites.
W. Y. Wang, J. Li, and X. He, “Deep reinforcement learning for nlp,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts , 2018, pp. 19–21
2018
Earlier work this paper cites.
G. Bonaccorso, Machine Learning Algorithms: Popular algorithms for data science and machine learning . Packt Publishing Ltd, 2018
2018
Earlier work this paper cites.
A. Fernández, S. Garcia, F. Herrera, and N. V. Chawla, “Smote for learning from imbalanced data: progress and challenges, marking the 15-year anniversary,” Journal of artificial intelligence research , vol. 61, pp. 863–905, 2018
2018
Earlier work this paper cites.
J. D. M.-W. C. Kenton and L. K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of NAACL-HLT , 2019, pp. 4171–4186
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
J. Li, S. Ji, T. Du, B. Li, and T. Wang, “Textbugger: Generating adversarial text against real-world applications,” Network and Distributed Systems Security (NDSS) Symposium , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
G. Montavon, A. Binder, S. Lapuschkin, W. Samek, and K.-R. Müller, “Layer-wise relevance propagation: an overview,” Explainable AI: Interpreting, Explaining and Visualizing Deep Learning , pp. 193–209, 2019
2019
Cited alongside, same era.
S. Ren, Y. Deng, K. He, and W. Che, “Generating natural language adversarial examples through probability weighted word saliency,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , 2019, pp. 1085–1097
2019
Cited alongside, same era.
2021
Later among the works it cites.
W. Wang, P. Tang, J. Lou, and L. Xiong, “Certified robustness to word substitution attack with differential privacy,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2021, pp. 1102–1112
2021
Later among the works it cites.
H. Chefer, S. Gur, and L. Wolf, “Transformer interpretability beyond attention visualization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 782–791
2021
Later among the works it cites.
C. Vladescu, M.-A. Dinisor, O. Grigorescu, D. Corlatescu, C. Sandescu, and M. Dascalu, “What are the latest cybersecurity trends? a case study grounded in language models,” in 2021 23rd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) . IEEE, 2021, pp. 140–146
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Luque, A. Carrasco, A. Martín, and A. de las Heras, “The impact of class imbalance in classification performance metrics based on the binary confusion matrix,” Pattern Recognition , vol. 91, pp. 216–231, 2019
2019
Cited alongside, same era.
A. Ilyas, S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry, “Adversarial examples are not bugs, they are features,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
C. Etmann, S. Lunz, P. Maass, and C. Schoenlieb, “On the connection between adversarial robustness and saliency map interpretability,” in International Conference on Machine Learning . PMLR, 2019, pp. 1823–1832
2019
Cited alongside, same era.
T. Zhang, V. Kishore, F. Wu, K. Q. Weinberger, and Y. Artzi, “Bertscore: Evaluating text generation with bert,” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
L. Li, R. Ma, Q. Guo, X. Xue, and X. Qiu, “BERT-ATTACK: Adversarial attack against BERT using BERT,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2020, pp. 6193–6202
2020
Cited alongside, same era.
D. Jin, Z. Jin, J. T. Zhou, and P. Szolovits, “Is bert really robust? a strong baseline for natural language attack on text classification and entailment,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 05, 2020, pp. 8018–8025
2020
Cited alongside, same era.
T. Le, S. Wang, and D. Lee, “Malcom: Generating malicious comments to attack neural fake news detection models,” in 2020 IEEE International Conference on Data Mining (ICDM) . IEEE, 2020, pp. 282–291
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2021
Later among the works it cites.
J. Lee, F. Tang, P. Ye, F. Abbasi, P. Hay, and D. M. Divakaran, “D-fence: A flexible, efficient, and comprehensive phishing email detection system,” in 2021 IEEE European Symposium on Security and Privacy (EuroS&P) . IEEE, 2021, pp. 578–597
2021
Later among the works it cites.
2021
Later among the works it cites.
X. Chen, A. Salem, D. Chen, M. Backes, S. Ma, Q. Shen, Z. Wu, and Y. Zhang, “Badnl: Backdoor attacks against nlp models with semantic-preserving improvements,” in Annual Computer Security Applications Conference , 2021, pp. 554–569
2021
Later among the works it cites.
T. Le, N. Park, and D. Lee, “Shield: Defending textual neural networks against multiple black-box adversarial attacks with stochastic multi-expert patcher,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2022, pp. 6661–6674
2022
Later among the works it cites.
E. Mosca, S. Agarwal, J. Rando Ramírez, and G. Groh, ““that is a suspicious reaction!”: Interpreting logits variation to detect NLP adversarial attacks,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics , May 2022, pp. 7806–7816
2022
Later among the works it cites.
K. Yoo, J. Kim, J. Jang, and N. Kwak, “Detection of adversarial examples in text classification: Benchmark and baseline via robust density estimation,” in Findings of the Association for Computational Linguistics: ACL 2022 , 2022, pp. 3656–3672
2022
Later among the works it cites.
L. Huber, M. A. Kühn, E. Mosca, and G. Groh, “Detecting word-level adversarial text attacks via shapley additive explanations,” in Proceedings of the 7th Workshop on Representation Learning for NLP , 2022, pp. 156–166
2022
Later among the works it cites.
B. Sabir, M. A. Babar, R. Gaire, and A. Abuadbba, “Reliability and robustness analysis of machine learning based phishing url detectors,” IEEE Transactions on Dependable and Secure Computing , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
K. A. Akbar, S. M. Halim, Y. Hu, A. Singhal, L. Khan, and B. Thuraisingham, “Knowledge mining in cybersecurity: From attack to defense,” in Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy . Springer, 2022, pp. 110–122
2022
Later among the works it cites.
X. Zhang, Z. Zhang, Q. Zhong, X. Zheng, Y. Zhang, S. Hu, and L. Y. Zhang, “Masked language model based textual adversarial example detection,” in Proceedings of the ACM Asia Conference on Computer and Communications Security , 2023, p. 925–937
2023
Closest in time.
2023
Closest in time.
M. Nauta, J. Trienes, S. Pathak, E. Nguyen, M. Peters, Y. Schmitt, J. Schlötterer, M. van Keulen, and C. Seifert, “From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai,” ACM Computing Surveys , vol. 55, no. 13s, pp. 1–42, 2023
2023
Closest in time.
M. Bosley, M. Jacobs-Harukawa, H. Licht, and A. Hoyle, “Do we still need bert in the age of gpt? comparing the benefits of domain-adaptation and in-context-learning approaches to using llms for political science research,” in 2023 Annual Meeting of the Midwest Political Science Association (MPSA) , 2023
2023
Closest in time.
D. A. Tarzanagh, B. Hou, B. Tong, Q. Long, and L. Shen, “Fairness-aware class imbalanced learning on multiple subgroups,” in Uncertainty in Artificial Intelligence . PMLR, 2023, pp. 2123–2133
2023
Closest in time.
L. Jing, X. Song, K. Ouyang, M. Jia, and L. Nie, “Multi-source semantic graph-based multimodal sarcasm explanation generation,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , A. Rogers, J. Boyd-Graber, and N. Okazaki, Eds. Toronto, Canada: Association for Computational Linguistics, Jul. 2023, pp. 11 349–11 361. [Online]. Available: https://aclanthology.org/2023.acl-long.635/
2023
Closest in time.
D. Yang and Q. Jin, “Attractive storyteller: Stylized visual storytelling with unpaired text,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , A. Rogers, J. Boyd-Graber, and N. Okazaki, Eds. Toronto, Canada: Association for Computational Linguistics, Jul. 2023, pp. 11 053–11 066. [Online]. Available: https://aclanthology.org/2023.acl-long.619/
2023
Closest in time.
C.-H. Chiang and H.-y. Lee, “Can large language models be an alternative to human evaluations?” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , A. Rogers, J. Boyd-Graber, and N. Okazaki, Eds. Toronto, Canada: Association for Computational Linguistics, Jul. 2023, pp. 15 607–15 631. [Online]. Available: https://aclanthology.org/2023.acl-long.870/
2023
Closest in time.
P. Schmidtova, S. Mahamood, S. Balloccu, O. Dusek, A. Gatt, D. Gkatzia, D. M. Howcroft, O. Platek, and A. Sivaprasad, “Automatic metrics in natural language generation: A survey of current evaluation practices,” in Proceedings of the 17th International Natural Language Generation Conference , S. Mahamood, N. L. Minh, and D. Ippolito, Eds. Tokyo, Japan: Association for Computational Linguistics, Sep. 2024, pp. 557–583. [Online]. Available: https://aclanthology.org/2024.inlg-main.44/
2024
Closest in time.