# Differentiating Between Human-Written and AI-Generated Texts Using Automatically Extracted Linguistic Features

arXiv: https://arxiv.org/abs/2407.03646 · HTML: https://mlsanity.com/paper/2407.03646/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 4 Jul 2024.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)
- [Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature](https://mlsanity.com/paper/2310.05130/) (2023)
- [A Survey of Large Language Models in Medicine: Progress, Application, and Challenge](https://mlsanity.com/paper/2311.05112/) (2023)
- [Locally Typical Sampling](https://mlsanity.com/paper/2202.00666/) (2022)
- [Contrasting Linguistic Patterns in Human and LLM-Generated News Text](https://mlsanity.com/paper/2308.09067/) (2023)
- [Do large language models resemble humans in language use?](https://mlsanity.com/paper/2303.08014/) (2023)
- [Differentiate ChatGPT-generated and Human-written Medical Texts](https://mlsanity.com/paper/2304.11567/) (2023)
- [PhonologyBench: Evaluating Phonological Skills of Large Language Models](https://mlsanity.com/paper/2404.02456/) (2024)
- [Detection of developmental language disorder in Cypriot Greek children using a neural network algorithm](https://mlsanity.com/paper/2311.15054/) (2023)
- [To Burst or Not to Burst: Generating and Quantifying Improbable Text](https://mlsanity.com/paper/2401.15476/) (2024)
- [Open Brain AI. Automatic Language Assessment](https://mlsanity.com/paper/2306.06693/) (2023)

## Built on by (most cited first, 5 of 5)

- [ESPERANTO: Evaluating Synthesized Phrases to Enhance Robustness in AI Detection for Text Origination](https://mlsanity.com/paper/2409.14285/) (2024)
- [Wikipedia in the Era of LLMs: Evolution and Risks](https://mlsanity.com/paper/2503.02879/) (2025)
- [HULLMI: Human vs LLM identification with explainability](https://mlsanity.com/paper/2409.04808/) (2024)
- [Can AI mimic the human ability to define neologisms?](https://mlsanity.com/paper/2502.14900/) (2025)
- [Explaining Generalization of AI-Generated Text Detectors Through Linguistic Analysis](https://mlsanity.com/paper/2601.07974/) (2026)

## Abstract (arXiv)

While extensive research has focused on ChatGPT in recent years, very few studies have systematically quantified and compared linguistic features between human-written and artificial intelligence (AI)-generated language. This exploratory study aims to investigate how various linguistic components are represented in both types of texts, assessing the ability of AI to emulate human writing. Using human-authored essays as a benchmark, we prompted ChatGPT to generate essays of equivalent length. These texts were analyzed using Open Brain AI, an online computational tool, to extract measures of phonological, morphological, syntactic, and lexical constituents. Despite AI-generated texts appearing to mimic human speech, the results revealed significant differences across multiple linguistic features such as specific types of consonants, nouns, adjectives, pronouns, adjectival/prepositional modifiers, and use of difficult words, among others. These findings underscore the importance of integrating automated tools for efficient language assessment, reducing time and effort in data analysis. Moreover, they emphasize the necessity for enhanced training methodologies to improve the engineering capacity of AI for producing more human-like text.