mdok of KInIT: Robustly Fine-tuned LLM for Binary and Multiclass AI-Generated Text Detection
This addresses the challenge of robust AI-generated text detection for preventing misuse like plagiarism and disinformation, though it appears incremental as it builds on existing fine-tuning methods.
The paper tackled the problem of detecting AI-generated text by fine-tuning smaller LLMs for classification, achieving first rank in both binary and multiclass detection tasks at the Voight-Kampff Generative AI Detection 2025.
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.