Hybrid Large Language Models (LLM) with Adversarial Training for Robust AI-Generated Text Classification

Abstract

The rapid advancement of Large Language Models (LLMs) such as GPT-4 and Bard has transformed text generation, yet it has also introduced challenges in distinguishing AI-generated content from human-written text. Ensuring authenticity in textual data is crucial in various domains, including academia, journalism, and content moderation. Traditional detection methods often struggle with adversarially manipulated text and paraphrased AI-generated content, limiting their effectiveness in real-world applications. This study proposes a hybrid detection model that integrates DistilBERT and RoBERTa to improve the identification of AI-generated text. The model is evaluated on two publicly available datasets, LLM-Detect AI Generated Text and DAIGT-V3 Train Dataset, achieving accuracy between 94.2% and 96.1%. By incorporating advanced attention mechanisms, multi-task learning, and adversarial training, the model enhances robustness against paraphrased and adversarially manipulated text, improving detection reliability. Additionally, adversarial data augmentation and FGSM-based adversarial training are employed to strengthen the model’s ability to classify text under real-world perturbations. The findings highlight the effectiveness of the proposed approach in mitigating the risks associated with AI-generated content. By leveraging a hybrid transformer-based architecture, the model provides a scalable, adaptable, and efficient solution for detecting AI-generated text across different linguistic and domain-specific contexts.