@inproceedings{xiong-etal-2024-enhance-robustness,
title = "Enhance Robustness of Language Models against Variation Attack through Graph Integration",
author = "Xiong, Zi and
Qing, Lizhi and
Kang, Yangyang and
Liu, Jiawei and
Li, Hongsong and
Sun, Changlong and
Liu, Xiaozhong and
Lu, Wei",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.520",
pages = "5866--5877",
abstract = "The widespread use of pre-trained language models (PLMs) in natural language processing (NLP) has greatly improved performance outcomes. However, these models{'} vulnerability to adversarial attacks (e.g., camouflaged hints from drug dealers), particularly in the Chinese language with its rich character diversity/variation and complex structures, hatches vital apprehension. In this study, we propose a novel method, CHinese vAriatioN Graph Enhancement (CHANGE), to increase the robustness of PLMs against character variation attacks in Chinese content. CHANGE presents a novel approach to incorporate a Chinese character variation graph into the PLMs. Through designing different supplementary tasks utilizing the graph structure, CHANGE essentially enhances PLMs{'} interpretation of adversarially manipulated text. Experiments conducted in a multitude of NLP tasks show that CHANGE outperforms current language models in combating against adversarial attacks and serves as a valuable contribution to robust language model research. Moreover, these findings highlight the substantial potential of graph-guided pre-training strategies for real-world applications.",
}
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<abstract>The widespread use of pre-trained language models (PLMs) in natural language processing (NLP) has greatly improved performance outcomes. However, these models’ vulnerability to adversarial attacks (e.g., camouflaged hints from drug dealers), particularly in the Chinese language with its rich character diversity/variation and complex structures, hatches vital apprehension. In this study, we propose a novel method, CHinese vAriatioN Graph Enhancement (CHANGE), to increase the robustness of PLMs against character variation attacks in Chinese content. CHANGE presents a novel approach to incorporate a Chinese character variation graph into the PLMs. Through designing different supplementary tasks utilizing the graph structure, CHANGE essentially enhances PLMs’ interpretation of adversarially manipulated text. Experiments conducted in a multitude of NLP tasks show that CHANGE outperforms current language models in combating against adversarial attacks and serves as a valuable contribution to robust language model research. Moreover, these findings highlight the substantial potential of graph-guided pre-training strategies for real-world applications.</abstract>
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%0 Conference Proceedings
%T Enhance Robustness of Language Models against Variation Attack through Graph Integration
%A Xiong, Zi
%A Qing, Lizhi
%A Kang, Yangyang
%A Liu, Jiawei
%A Li, Hongsong
%A Sun, Changlong
%A Liu, Xiaozhong
%A Lu, Wei
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F xiong-etal-2024-enhance-robustness
%X The widespread use of pre-trained language models (PLMs) in natural language processing (NLP) has greatly improved performance outcomes. However, these models’ vulnerability to adversarial attacks (e.g., camouflaged hints from drug dealers), particularly in the Chinese language with its rich character diversity/variation and complex structures, hatches vital apprehension. In this study, we propose a novel method, CHinese vAriatioN Graph Enhancement (CHANGE), to increase the robustness of PLMs against character variation attacks in Chinese content. CHANGE presents a novel approach to incorporate a Chinese character variation graph into the PLMs. Through designing different supplementary tasks utilizing the graph structure, CHANGE essentially enhances PLMs’ interpretation of adversarially manipulated text. Experiments conducted in a multitude of NLP tasks show that CHANGE outperforms current language models in combating against adversarial attacks and serves as a valuable contribution to robust language model research. Moreover, these findings highlight the substantial potential of graph-guided pre-training strategies for real-world applications.
%U https://aclanthology.org/2024.lrec-main.520
%P 5866-5877
Markdown (Informal)
[Enhance Robustness of Language Models against Variation Attack through Graph Integration](https://aclanthology.org/2024.lrec-main.520) (Xiong et al., LREC-COLING 2024)
ACL
- Zi Xiong, Lizhi Qing, Yangyang Kang, Jiawei Liu, Hongsong Li, Changlong Sun, Xiaozhong Liu, and Wei Lu. 2024. Enhance Robustness of Language Models against Variation Attack through Graph Integration. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 5866–5877, Torino, Italia. ELRA and ICCL.