Pengwei Zhan
Orcid: 0000-0003-3724-4431
According to our database1,
Pengwei Zhan
authored at least 14 papers
between 2021 and 2024.
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Bibliography
2024
Proceedings of the Advanced Intelligent Computing Technology and Applications, 2024
Unveiling the Lexical Sensitivity of LLMs: Combinatorial Optimization for Prompt Enhancement.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024
Rethinking Word-level Adversarial Attack: The Trade-off between Efficiency, Effectiveness, and Imperceptibility.
Proceedings of the 2024 Joint International Conference on Computational Linguistics, 2024
2023
Unsupervised Clustering with Contrastive Learning for Rumor Tracking on Social Media.
Proceedings of the Natural Language Processing and Chinese Computing, 2023
Improving the Quality of Textual Adversarial Examples with Dynamic N-gram Based Attack.
Proceedings of the 26th International Conference on Computer Supported Cooperative Work in Design, 2023
Similarizing the Influence of Words with Contrastive Learning to Defend Word-level Adversarial Text Attack.
Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023
Contrastive Learning with Adversarial Examples for Alleviating Pathology of Language Model.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023
2022
Proceedings of the International Joint Conference on Neural Networks, 2022
SP Attack: Single-Perspective Attack for Generating Adversarial Omnidirectional Images.
Proceedings of the IEEE International Conference on Acoustics, 2022
Proceedings of the 25th IEEE International Conference on Computer Supported Cooperative Work in Design, 2022
Proceedings of the 29th International Conference on Computational Linguistics, 2022
Mitigating the Inconsistency Between Word Saliency and Model Confidence with Pathological Contrastive Training.
Proceedings of the Findings of the Association for Computational Linguistics: ACL 2022, 2022
2021
Proceedings of the Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, 2021