Decoding AI and Human Authorship: Nuances Revealed Through NLP and Statistical Analysis

TL;DR

This study analyzes linguistic differences between AI and human texts using statistical methods on a large dataset, revealing key distinctions in vocabulary, creativity, and novelty to better understand AI's language capabilities.

Contribution

It provides a detailed statistical comparison of AI and human writing, highlighting new insights into linguistic traits, creativity, and biases in AI-generated texts.

Findings

Humans have higher vocabulary diversity than AI.

AI-generated texts show slightly higher novelty.

Humans produce longer but shorter words on average.

Abstract

This research explores the nuanced differences in texts produced by AI and those written by humans, aiming to elucidate how language is expressed differently by AI and humans. Through comprehensive statistical data analysis, the study investigates various linguistic traits, patterns of creativity, and potential biases inherent in human-written and AI- generated texts. The significance of this research lies in its contribution to understanding AI's creative capabilities and its impact on literature, communication, and societal frameworks. By examining a meticulously curated dataset comprising 500K essays spanning diverse topics and genres, generated by LLMs, or written by humans, the study uncovers the deeper layers of linguistic expression and provides insights into the cognitive processes underlying both AI and human-driven textual compositions. The analysis revealed that human-authored essays tend to have a higher total word count on average than AI-generated essays but have a shorter average word length compared to AI- generated essays, and while both groups exhibit high levels of fluency, the vocabulary diversity of Human authored content is higher than AI generated content. However, AI- generated essays show a slightly higher level of novelty, suggesting the potential for generating more original content through AI systems. The paper addresses challenges in assessing the language generation capabilities of AI models and emphasizes the importance of datasets that reflect the complexities of human-AI collaborative writing. Through systematic preprocessing and rigorous statistical analysis, this study offers valuable insights into the evolving landscape of AI-generated content and informs future developments in natural language processing (NLP).