@inproceedings{guo-etal-2024-ustc,
title = "{USTC}-{BUPT} at {S}em{E}val-2024 Task 8: Enhancing Machine-Generated Text Detection via Domain Adversarial Neural Networks and {LLM} Embeddings",
author = "Guo, Zikang and
Jiao, Kaijie and
Yao, Xingyu and
Wan, Yuning and
Li, Haoran and
Xu, Benfeng and
Zhang, Licheng and
Wang, Quan and
Zhang, Yongdong and
Mao, Zhendong",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.217/",
doi = "10.18653/v1/2024.semeval-1.217",
pages = "1511--1522",
abstract = "This paper introduces the system developed by USTC-BUPT for SemEval-2024 Task 8. The shared task comprises three subtasks across four tracks, aiming to develop automatic systems to distinguish between human-written and machine-generated text across various domains, languages and generators. Our system comprises four components: DATeD, LLAM, TLE, and AuDM, which empower us to effectively tackle all subtasks posed by the challenge. In the monolingual track, DATeD improves machine-generated text detection by incorporating a gradient reversal layer and integrating additional domain labels through Domain Adversarial Neural Networks, enhancing adaptation to diverse text domains. In the multilingual track, LLAM employs different strategies based on language characteristics. For English text, the LLM Embeddings approach utilizes embeddings from a proxy LLM followed by a two-stage CNN for classification, leveraging the broad linguistic knowledge captured during pre-training to enhance performance. For text in other languages, the LLM Sentinel approach transforms the classification task into a next-token prediction task, which facilitates easier adaptation to texts in various languages, especially low-resource languages. TLE utilizes the LLM Embeddings method with a minor modification in the classification strategy for subtask B. AuDM employs data augmentation and fine-tunes the DeBERTa model specifically for subtask C. Our system wins the multilingual track and ranks second in the monolingual track. Additionally, it achieves third place in both subtask B and C."
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<abstract>This paper introduces the system developed by USTC-BUPT for SemEval-2024 Task 8. The shared task comprises three subtasks across four tracks, aiming to develop automatic systems to distinguish between human-written and machine-generated text across various domains, languages and generators. Our system comprises four components: DATeD, LLAM, TLE, and AuDM, which empower us to effectively tackle all subtasks posed by the challenge. In the monolingual track, DATeD improves machine-generated text detection by incorporating a gradient reversal layer and integrating additional domain labels through Domain Adversarial Neural Networks, enhancing adaptation to diverse text domains. In the multilingual track, LLAM employs different strategies based on language characteristics. For English text, the LLM Embeddings approach utilizes embeddings from a proxy LLM followed by a two-stage CNN for classification, leveraging the broad linguistic knowledge captured during pre-training to enhance performance. For text in other languages, the LLM Sentinel approach transforms the classification task into a next-token prediction task, which facilitates easier adaptation to texts in various languages, especially low-resource languages. TLE utilizes the LLM Embeddings method with a minor modification in the classification strategy for subtask B. AuDM employs data augmentation and fine-tunes the DeBERTa model specifically for subtask C. Our system wins the multilingual track and ranks second in the monolingual track. Additionally, it achieves third place in both subtask B and C.</abstract>
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%0 Conference Proceedings
%T USTC-BUPT at SemEval-2024 Task 8: Enhancing Machine-Generated Text Detection via Domain Adversarial Neural Networks and LLM Embeddings
%A Guo, Zikang
%A Jiao, Kaijie
%A Yao, Xingyu
%A Wan, Yuning
%A Li, Haoran
%A Xu, Benfeng
%A Zhang, Licheng
%A Wang, Quan
%A Zhang, Yongdong
%A Mao, Zhendong
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F guo-etal-2024-ustc
%X This paper introduces the system developed by USTC-BUPT for SemEval-2024 Task 8. The shared task comprises three subtasks across four tracks, aiming to develop automatic systems to distinguish between human-written and machine-generated text across various domains, languages and generators. Our system comprises four components: DATeD, LLAM, TLE, and AuDM, which empower us to effectively tackle all subtasks posed by the challenge. In the monolingual track, DATeD improves machine-generated text detection by incorporating a gradient reversal layer and integrating additional domain labels through Domain Adversarial Neural Networks, enhancing adaptation to diverse text domains. In the multilingual track, LLAM employs different strategies based on language characteristics. For English text, the LLM Embeddings approach utilizes embeddings from a proxy LLM followed by a two-stage CNN for classification, leveraging the broad linguistic knowledge captured during pre-training to enhance performance. For text in other languages, the LLM Sentinel approach transforms the classification task into a next-token prediction task, which facilitates easier adaptation to texts in various languages, especially low-resource languages. TLE utilizes the LLM Embeddings method with a minor modification in the classification strategy for subtask B. AuDM employs data augmentation and fine-tunes the DeBERTa model specifically for subtask C. Our system wins the multilingual track and ranks second in the monolingual track. Additionally, it achieves third place in both subtask B and C.
%R 10.18653/v1/2024.semeval-1.217
%U https://aclanthology.org/2024.semeval-1.217/
%U https://doi.org/10.18653/v1/2024.semeval-1.217
%P 1511-1522
Markdown (Informal)
[USTC-BUPT at SemEval-2024 Task 8: Enhancing Machine-Generated Text Detection via Domain Adversarial Neural Networks and LLM Embeddings](https://aclanthology.org/2024.semeval-1.217/) (Guo et al., SemEval 2024)
ACL
- Zikang Guo, Kaijie Jiao, Xingyu Yao, Yuning Wan, Haoran Li, Benfeng Xu, Licheng Zhang, Quan Wang, Yongdong Zhang, and Zhendong Mao. 2024. USTC-BUPT at SemEval-2024 Task 8: Enhancing Machine-Generated Text Detection via Domain Adversarial Neural Networks and LLM Embeddings. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1511–1522, Mexico City, Mexico. Association for Computational Linguistics.