Publication Date

5-2026

School

School of Business

Major

Computer Science

Keywords

AI, LLM, stylometry

Disciplines

Artificial Intelligence and Robotics | Computer Engineering | Computer Sciences | Data Science | Other Computer Sciences

Abstract

Authorship attribution is an important topic in today’s world of Large Language Models (LLMs). It is the technology that helps to verify the author of a written work. This study explores whether LLMs can successfully mimic an individual’s writing style if they are given a text sample. A dataset of human-written texts was collected and used to prompt several LLMs to generate new texts that attempt to replicate the original author’s stylistic characteristics. The generated texts were then tested with modern authorship attribution models to determine whether they would be identified as being written by the original author. The results suggested that the LLMs could produce certain writing patterns, but they were generally distinguishable from the original author by modern authorship attribution systems. These findings suggest that authorship attribution techniques remain effective in differentiating authentic texts from AI-generated imitations, even when models are explicitly prompted to mimic a specific writing style and provided with writing samples.

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