{"type":"ScholarlyArticle","title":"MAT: Mixed-Strategy Game of Adversarial Training in Fine-tuning","authors":[{"name":"Zhehua Zhong","slug":"zhehua-zhong","given":"Zhehua","family":"Zhong","affiliation":"School of Cyberspace , Hangzhou Dianzi University"},{"name":"Tianyi Chen","slug":"tianyi-chen","given":"Tianyi","family":"Chen","affiliation":"Microsoft"},{"name":"Zhen Wang","slug":"zhen-wang","given":"Zhen","family":"Wang","affiliation":"School of Cyberspace , Hangzhou Dianzi University"}],"abstract":"Fine-tuning large-scale pre-trained language models has been demonstrated effective for various natural language processing (NLP) tasks. Previous studies have established that incorporating adversarial training during the fine-tuning stage can significantly enhance model generalization and robustness. However, from the perspective of game theory, such utilizations of adversarial training correspond to pure-strategy games, which are inherently limited in terms of the scope of their strategies, thereby still having room for improvement. In order to push the performance boundaries, we propose a novel Mixed-strategy Adversarial Training algorithm (MAT). Methodologically, we derive the Nash equilibrium of a mixed-strategy game for adversarial training using Entropy Mirror Descent to establish MAT by sampling method. To verify the effectiveness of MAT, we conducted extensive benchmark experiments on large-scale pre-trained models, such as BERT and RoBERTa. MAT significantly outperforms the state-of-the-art methods on both the GLUE and ANLI benchmarks in terms of generalization and robustness.","doi":"10.24963/ijcai.2023/520","url":"https://ijcai.org/proceedings/2023/520","pdf":"https://ijcai.org/proceedings/2023/0520.pdf","markdown":"https://ijcai.org/proceedings/2023/520.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/520","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":520,"pages":{"first":4674,"last":4683},"keywords":["Natural Language Processing: NLP: Other","Machine Learning: ML: Adversarial machine learning"],"videos":[],"references":[{"position":1,"raw":"[Aghajanyan et al., 2021] Armen Aghajanyan, Akshat Shri- vastava, Anchit Gupta, Naman Goyal, Luke Zettlemoyer, and Sonal Gupta. 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