Artificial Intelligence vs. Human: Decoding Text Authenticity with Transformers

This paper presents a comprehensive study on detecting AI-generated text using transformer models. Our research extends the existing RODICA dataset to create the Enhanced RODICA for Human-Authored and AI-Generated Text (ERH) dataset. We enriched RODICA by incorporating machine-generated texts from various large language models (LLMs), ensuring a diverse and representative corpus. Methodologically, we fine-tuned several transformer architectures, including BERT, RoBERTa, and DistilBERT, on this dataset to distinguish between human-written and AI-generated text. Our experiments examined both monolingual and multilingual settings, evaluating the model’s performance across diverse datasets such as M4, AICrowd, Indonesian Hoax News Detection, TURNBACKHOAX, and ERH. The results demonstrate that RoBERTa-large achieved superior accuracy and F-scores of around 83%, particularly in monolingual contexts, while DistilBERT-multilingual-cased excelled in multilingual scenarios, achieving accuracy and F-scores of around 72%. This study contributes a refined dataset and provides insights into model performance, highlighting the transformative potential of transformer models in detecting AI-generated content.