mdok of KInIT: Robustly Fine-tuned LLM for Binary and Multiclass AI-Generated Text Detection
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912602100072448 |
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| author | Macko, Dominik |
| author_facet | Macko, Dominik |
| contents | The large language models (LLMs) are able to generate high-quality texts in multiple languages. Such texts are often not recognizable by humans as generated, and therefore present a potential of LLMs for misuse (e.g., plagiarism, spams, disinformation spreading). An automated detection is able to assist humans to indicate the machine-generated texts; however, its robustness to out-of-distribution data is still challenging. This notebook describes our mdok approach in robust detection, based on fine-tuning smaller LLMs for text classification. It is applied to both subtasks of Voight-Kampff Generative AI Detection 2025, providing remarkable performance (1st rank) in both, the binary detection as well as the multiclass classification of various cases of human-AI collaboration. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_01702 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | mdok of KInIT: Robustly Fine-tuned LLM for Binary and Multiclass AI-Generated Text Detection Macko, Dominik Computation and Language The large language models (LLMs) are able to generate high-quality texts in multiple languages. Such texts are often not recognizable by humans as generated, and therefore present a potential of LLMs for misuse (e.g., plagiarism, spams, disinformation spreading). An automated detection is able to assist humans to indicate the machine-generated texts; however, its robustness to out-of-distribution data is still challenging. This notebook describes our mdok approach in robust detection, based on fine-tuning smaller LLMs for text classification. It is applied to both subtasks of Voight-Kampff Generative AI Detection 2025, providing remarkable performance (1st rank) in both, the binary detection as well as the multiclass classification of various cases of human-AI collaboration. |
| title | mdok of KInIT: Robustly Fine-tuned LLM for Binary and Multiclass AI-Generated Text Detection |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.01702 |