Classification of Human- and AI-Generated Texts for English, French, German, and Spanish
This work addresses the need for multilingual AI text detection, which is incremental as it extends existing methods to new languages and scenarios.
The paper tackled the problem of classifying human- and AI-generated texts across English, French, German, and Spanish, achieving high F1-scores up to 99% for AI-generated text detection and up to 86% for AI-rephrased text detection.
In this paper we analyze features to classify human- and AI-generated text for English, French, German and Spanish and compare them across languages. We investigate two scenarios: (1) The detection of text generated by AI from scratch, and (2) the detection of text rephrased by AI. For training and testing the classifiers in this multilingual setting, we created a new text corpus covering 10 topics for each language. For the detection of AI-generated text, the combination of all proposed features performs best, indicating that our features are portable to other related languages: The F1-scores are close with 99% for Spanish, 98% for English, 97% for German and 95% for French. For the detection of AI-rephrased text, the systems with all features outperform systems with other features in many cases, but using only document features performs best for German (72%) and Spanish (86%) and only text vector features leads to best results for English (78%).