@inproceedings{jiao-etal-2025-rangedetector,
title = "{M}-{R}ange{D}etector: Enhancing Generalization in Machine-Generated Text Detection through Multi-Range Attention Masks",
author = "Jiao, Kaijie and
Wang, Quan and
Zhang, Licheng and
Guo, Zikang and
Mao, Zhendong",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.469/",
doi = "10.18653/v1/2025.findings-acl.469",
pages = "8971--8983",
ISBN = "979-8-89176-256-5",
abstract = "The increasing capability and widespread usage of large language models (LLMs) highlight the desirability of automatic detection of machine-generated text. Existing supervised detectors often overfit within their training domains, as they have primarily learned domain-specific textual features, such as word frequency, syntax, and semantics. In this paper, we introduce a domain-independent feature, namely the difference of writing strategy between LLMs and human, to improve the out-of-domain generalization capability of detectors. LLMs focus on the preceding range tokens when generating a token, while human consider multiple ranges, including bidirectional, global, and local contexts. The attention mask influences the range of tokens to which the model can attend. Therefore, we propose a method called M-RangeDetector, which integrates four distinct attention masking strategies into a Multi-Range Attention module, enabling the model to capture diverse writing strategies. Specifically, with the global mask, band mask, dilated mask, and random mask, our method learns various writing strategies for machine-generated text detection. The experimental results on three datasets demonstrate the superior generalization capability of our method."
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<abstract>The increasing capability and widespread usage of large language models (LLMs) highlight the desirability of automatic detection of machine-generated text. Existing supervised detectors often overfit within their training domains, as they have primarily learned domain-specific textual features, such as word frequency, syntax, and semantics. In this paper, we introduce a domain-independent feature, namely the difference of writing strategy between LLMs and human, to improve the out-of-domain generalization capability of detectors. LLMs focus on the preceding range tokens when generating a token, while human consider multiple ranges, including bidirectional, global, and local contexts. The attention mask influences the range of tokens to which the model can attend. Therefore, we propose a method called M-RangeDetector, which integrates four distinct attention masking strategies into a Multi-Range Attention module, enabling the model to capture diverse writing strategies. Specifically, with the global mask, band mask, dilated mask, and random mask, our method learns various writing strategies for machine-generated text detection. The experimental results on three datasets demonstrate the superior generalization capability of our method.</abstract>
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%0 Conference Proceedings
%T M-RangeDetector: Enhancing Generalization in Machine-Generated Text Detection through Multi-Range Attention Masks
%A Jiao, Kaijie
%A Wang, Quan
%A Zhang, Licheng
%A Guo, Zikang
%A Mao, Zhendong
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F jiao-etal-2025-rangedetector
%X The increasing capability and widespread usage of large language models (LLMs) highlight the desirability of automatic detection of machine-generated text. Existing supervised detectors often overfit within their training domains, as they have primarily learned domain-specific textual features, such as word frequency, syntax, and semantics. In this paper, we introduce a domain-independent feature, namely the difference of writing strategy between LLMs and human, to improve the out-of-domain generalization capability of detectors. LLMs focus on the preceding range tokens when generating a token, while human consider multiple ranges, including bidirectional, global, and local contexts. The attention mask influences the range of tokens to which the model can attend. Therefore, we propose a method called M-RangeDetector, which integrates four distinct attention masking strategies into a Multi-Range Attention module, enabling the model to capture diverse writing strategies. Specifically, with the global mask, band mask, dilated mask, and random mask, our method learns various writing strategies for machine-generated text detection. The experimental results on three datasets demonstrate the superior generalization capability of our method.
%R 10.18653/v1/2025.findings-acl.469
%U https://aclanthology.org/2025.findings-acl.469/
%U https://doi.org/10.18653/v1/2025.findings-acl.469
%P 8971-8983
Markdown (Informal)
[M-RangeDetector: Enhancing Generalization in Machine-Generated Text Detection through Multi-Range Attention Masks](https://aclanthology.org/2025.findings-acl.469/) (Jiao et al., Findings 2025)
ACL