A Dual-Approach for AI-Generated Text Detection

The proliferation of sophisticated AI generative models like GPT-4 has revolutionized natural language processing (NLP) but also raised critical concerns about content authenticity in academia, media, and digital communications. This paper introduces a dual-approach AI-generated text detector that leverages both traditional machine learning (ML) techniques and advanced fine-tuned large language models (LLMs). Utilizing a comprehensive dataset of over 350,000 samples from five bench-mark sources, our approach demonstrated robust performance, with conventional ML methods achieving 91–92 % accuracy (0.97 ROC-AUC) and fine-tuned LLMs such as BERT and RoBERTa reaching 97-98% accuracy (0.99 ROC-AUC). We developed TruAIText, a practical tool that integrates these models to provide detailed analysis of AI-generated content, including paragraph-level probabilities. Despite its efficacy, the tool requires ongoing updates to counteract adversarial manipulation.