mdok of KInIT: Robustly Fine-tuned LLM for Binary and Multiclass AI-Generated Text Detection

• URL: http://arxiv.org/abs/2506.01702v1
• Date: Mon, 02 Jun 2025 14:07:32 GMT
• Title: mdok of KInIT: Robustly Fine-tuned LLM for Binary and Multiclass AI-Generated Text Detection
• Authors: Dominik Macko,
• Abstract summary: An automated detection is able to assist humans to indicate the machine-generated texts.<n>This notebook describes our mdok approach in robust detection, based on fine-tuning smaller LLMs for text classification.<n>It is applied to both subtasks of Voight-Kampff Generative AI Detection 2025.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 in binary detection as well as in multiclass (1st rank) classification of various cases of human-AI collaboration.

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