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arXiv · 2307.12166

The Imitation Game: Detecting Human and AI-Generated Texts in the Era of ChatGPT and BARD

Kadhim Hayawi·Sakib Shahriar·Sujith Samuel Mathew
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Abstract

The potential of artificial intelligence (AI)-based large language models (LLMs) holds considerable promise in revolutionizing education, research, and practice. However, distinguishing between human-written and AI-generated text has become a significant task. This paper presents a comparative study, introducing a novel dataset of human-written and LLM-generated texts in different genres: essays, stories, poetry, and Python code. We employ several machine learning models to classify the texts. Results demonstrate the efficacy of these models in discerning between human and AI-generated text, despite the dataset's limited sample size. However, the task becomes more challenging when classifying GPT-generated text, particularly in story writing. The results indicate that the models exhibit superior performance in binary classification tasks, such as distinguishing human-generated text from a specific LLM, compared to the more complex multiclass tasks that involve discerning among human-generated and multiple LLMs. Our findings provide insightful implications for AI text detection while our dataset paves the way for future research in this evolving area.

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Kadhim Hayawi, Sakib Shahriar, Sujith Samuel Mathew. 2023-07-22. The Imitation Game: Detecting Human and AI-Generated Texts in the Era of ChatGPT and BARD. https://arxiv.org/abs/2307.12166

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