Toward Robust Generative AI Text Detection: Generalizable Neural Model

Large Language Models (LLMs) have demonstrated remarkable capabilities in generating text that closely resembles human writing across wide range of styles and genres. However, such capabilities are prone to potential misuse, such as fake news generation, spam email creation, and misuse in academic assignments. Hence, it is essential to build automated approaches capable of distinguishing between Artificial Intelligence-generated text and human-authored text. In this paper, we proposed a fine-tuning based neural model which is a combination of transformer models, linguistic features and state-of-the-art embedding models. We have also curated a training dataset encompassing diverse samples from different LLMs and domains to fine-tune pretrained language models. We evaluated our model's performance against state-of-the-art methods, and the comparative analysis demonstrates that our approach consistently outperforms other methods across various datasets using established evaluation metrics.