@inproceedings{yu-etal-2024-text,
title = "Text Fluoroscopy: Detecting {LLM}-Generated Text through Intrinsic Features",
author = "Yu, Xiao and
Chen, Kejiang and
Yang, Qi and
Zhang, Weiming and
Yu, Nenghai",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.885/",
doi = "10.18653/v1/2024.emnlp-main.885",
pages = "15838--15846",
abstract = "Large language models (LLMs) have revolutionized the domain of natural language processing because of their excellent performance on various tasks. Despite their impressive capabilities, LLMs also have the potential to generate texts that pose risks of misuse. Consequently, detecting LLM-generated text has become increasingly important.Previous LLM-generated text detection methods use semantic features, which are stored in the last layer. This leads to methods that overfit the training set domain and exhibit shortcomings in generalization. Therefore, We argue that utilizing intrinsic features rather than semantic features for detection results in better performance.In this work, we design Text Fluoroscopy, a black-box method with better generalizability for detecting LLM-generated text by mining the intrinsic features of the text to be detected. Our method captures the text`s intrinsic features by identifying the layer with the largest distribution difference from the last and first layers when projected to the vocabulary space.Our method achieves 7.36{\%} and 2.84{\%} average improvement in detection performance compared to the baselines in detecting texts from different domains generated by GPT-4 and Claude3, respectively."
}
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<abstract>Large language models (LLMs) have revolutionized the domain of natural language processing because of their excellent performance on various tasks. Despite their impressive capabilities, LLMs also have the potential to generate texts that pose risks of misuse. Consequently, detecting LLM-generated text has become increasingly important.Previous LLM-generated text detection methods use semantic features, which are stored in the last layer. This leads to methods that overfit the training set domain and exhibit shortcomings in generalization. Therefore, We argue that utilizing intrinsic features rather than semantic features for detection results in better performance.In this work, we design Text Fluoroscopy, a black-box method with better generalizability for detecting LLM-generated text by mining the intrinsic features of the text to be detected. Our method captures the text‘s intrinsic features by identifying the layer with the largest distribution difference from the last and first layers when projected to the vocabulary space.Our method achieves 7.36% and 2.84% average improvement in detection performance compared to the baselines in detecting texts from different domains generated by GPT-4 and Claude3, respectively.</abstract>
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%0 Conference Proceedings
%T Text Fluoroscopy: Detecting LLM-Generated Text through Intrinsic Features
%A Yu, Xiao
%A Chen, Kejiang
%A Yang, Qi
%A Zhang, Weiming
%A Yu, Nenghai
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F yu-etal-2024-text
%X Large language models (LLMs) have revolutionized the domain of natural language processing because of their excellent performance on various tasks. Despite their impressive capabilities, LLMs also have the potential to generate texts that pose risks of misuse. Consequently, detecting LLM-generated text has become increasingly important.Previous LLM-generated text detection methods use semantic features, which are stored in the last layer. This leads to methods that overfit the training set domain and exhibit shortcomings in generalization. Therefore, We argue that utilizing intrinsic features rather than semantic features for detection results in better performance.In this work, we design Text Fluoroscopy, a black-box method with better generalizability for detecting LLM-generated text by mining the intrinsic features of the text to be detected. Our method captures the text‘s intrinsic features by identifying the layer with the largest distribution difference from the last and first layers when projected to the vocabulary space.Our method achieves 7.36% and 2.84% average improvement in detection performance compared to the baselines in detecting texts from different domains generated by GPT-4 and Claude3, respectively.
%R 10.18653/v1/2024.emnlp-main.885
%U https://aclanthology.org/2024.emnlp-main.885/
%U https://doi.org/10.18653/v1/2024.emnlp-main.885
%P 15838-15846
Markdown (Informal)
[Text Fluoroscopy: Detecting LLM-Generated Text through Intrinsic Features](https://aclanthology.org/2024.emnlp-main.885/) (Yu et al., EMNLP 2024)
ACL