Evaluating the Efficacy of Hybrid Deep Learning Models in Distinguishing AI-Generated Text

TL;DR

This paper evaluates hybrid deep learning models' effectiveness in distinguishing AI-generated text from human writing using advanced NLP techniques and a diverse dataset.

Contribution

It introduces a novel hybrid deep learning approach specifically designed for accurate differentiation between AI and human text.

Findings

High accuracy achieved in distinguishing AI from human text

Effective use of diverse datasets enhances model robustness

Advanced NLP features improve detection of nuanced differences

Abstract

My research investigates the use of cutting-edge hybrid deep learning models to accurately differentiate between AI-generated text and human writing. I applied a robust methodology, utilising a carefully selected dataset comprising AI and human texts from various sources, each tagged with instructions. Advanced natural language processing techniques facilitated the analysis of textual features. Combining sophisticated neural networks, the custom model enabled it to detect nuanced differences between AI and human content.