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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.15683 |
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| _version_ | 1866912438612393984 |
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| author | Shi, Yuhui Yang, Yehan Sheng, Qiang Mi, Hao Hu, Beizhe Xu, Chaoxi Cao, Juan |
| author_facet | Shi, Yuhui Yang, Yehan Sheng, Qiang Mi, Hao Hu, Beizhe Xu, Chaoxi Cao, Juan |
| contents | With the popularity of large language models (LLMs), undesirable societal problems like misinformation production and academic misconduct have been more severe, making LLM-generated text detection now of unprecedented importance. Although existing methods have made remarkable progress, a new challenge posed by text from privately tuned LLMs remains underexplored. Users could easily possess private LLMs by fine-tuning an open-source one with private corpora, resulting in a significant performance drop of existing detectors in practice. To address this issue, we propose PhantomHunter, an LLM-generated text detector specialized for detecting text from unseen, privately-tuned LLMs. Its family-aware learning framework captures family-level traits shared across the base models and their derivatives, instead of memorizing individual characteristics. Experiments on data from LLaMA, Gemma, and Mistral families show its superiority over 7 baselines and 3 industrial services, with F1 scores of over 96%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15683 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PhantomHunter: Detecting Unseen Privately-Tuned LLM-Generated Text via Family-Aware Learning Shi, Yuhui Yang, Yehan Sheng, Qiang Mi, Hao Hu, Beizhe Xu, Chaoxi Cao, Juan Computation and Language Computers and Society With the popularity of large language models (LLMs), undesirable societal problems like misinformation production and academic misconduct have been more severe, making LLM-generated text detection now of unprecedented importance. Although existing methods have made remarkable progress, a new challenge posed by text from privately tuned LLMs remains underexplored. Users could easily possess private LLMs by fine-tuning an open-source one with private corpora, resulting in a significant performance drop of existing detectors in practice. To address this issue, we propose PhantomHunter, an LLM-generated text detector specialized for detecting text from unseen, privately-tuned LLMs. Its family-aware learning framework captures family-level traits shared across the base models and their derivatives, instead of memorizing individual characteristics. Experiments on data from LLaMA, Gemma, and Mistral families show its superiority over 7 baselines and 3 industrial services, with F1 scores of over 96%. |
| title | PhantomHunter: Detecting Unseen Privately-Tuned LLM-Generated Text via Family-Aware Learning |
| topic | Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2506.15683 |