Humanizing Text with AI: A Natural Language Processing Approach Using Pre-Trained Language Models

Abstract: In recent years, the distinction between human-written and AI-generated text has become increasingly perceptible due to advancements in AI content detection systems. This paper explores a novel approach to humanizing AI-generated text using pre-trained language models in an offline environment. We present a modular pipeline built on the Mistral-7B model that progressively transforms machine-generated content into natural, human-like text through linguistic rephrasing, disfluencies, emotional tone shifts, and informal patterns. The system is implemented across six evolving applications, each designed to reduce the detectability of AI-generated content. Our methodology focuses on integrating semantic awareness and personalized stylistic elements such as contractions, filler words, and side-comments — mimicking how people naturally communicate. Unlike traditional API-based systems, our model runs entirely offline, ensuring data privacy, customization, and scalability. This framework offers a practical tool for enhancing the relatability and authenticity of AI-generated text in educational, creative, and professional contexts. It also contributes to ongoing conversations about machine authorship, text realism, and the ethical boundaries of content transformation.