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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2504.02873 |
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| _version_ | 1866912307947241472 |
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| author | Wei, Dongjun Mao, Minjia Fang, Xiao Chau, Michael |
| author_facet | Wei, Dongjun Mao, Minjia Fang, Xiao Chau, Michael |
| contents | The malicious usage of large language models (LLMs) has motivated the detection of LLM-generated texts. Previous work in topological data analysis shows that the persistent homology dimension (PHD) of text embeddings can serve as a more robust and promising score than other zero-shot methods. However, effectively detecting short LLM-generated texts remains a challenge. This paper presents Short-PHD, a zero-shot LLM-generated text detection method tailored for short texts. Short-PHD stabilizes the estimation of the previous PHD method for short texts by inserting off-topic content before the given input text and identifies LLM-generated text based on an established detection threshold. Experimental results on both public and generated datasets demonstrate that Short-PHD outperforms existing zero-shot methods in short LLM-generated text detection. Implementation codes are available online. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02873 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Short-PHD: Detecting Short LLM-generated Text with Topological Data Analysis After Off-topic Content Insertion Wei, Dongjun Mao, Minjia Fang, Xiao Chau, Michael Computation and Language The malicious usage of large language models (LLMs) has motivated the detection of LLM-generated texts. Previous work in topological data analysis shows that the persistent homology dimension (PHD) of text embeddings can serve as a more robust and promising score than other zero-shot methods. However, effectively detecting short LLM-generated texts remains a challenge. This paper presents Short-PHD, a zero-shot LLM-generated text detection method tailored for short texts. Short-PHD stabilizes the estimation of the previous PHD method for short texts by inserting off-topic content before the given input text and identifies LLM-generated text based on an established detection threshold. Experimental results on both public and generated datasets demonstrate that Short-PHD outperforms existing zero-shot methods in short LLM-generated text detection. Implementation codes are available online. |
| title | Short-PHD: Detecting Short LLM-generated Text with Topological Data Analysis After Off-topic Content Insertion |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2504.02873 |