# Comparing LLM-generated and human-authored news text using formal syntactic theory

arXiv: https://arxiv.org/abs/2506.01407 · HTML: https://mlsanity.com/paper/2506.01407/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [The Falcon Series of Open Language Models](https://mlsanity.com/paper/2311.16867/) (2023)
- [Contrasting Linguistic Patterns in Human and LLM-Generated News Text](https://mlsanity.com/paper/2308.09067/) (2023)
- [Do LLMs write like humans? Variation in grammatical and rhetorical styles](https://mlsanity.com/paper/2410.16107/) (2024)
- [Detection and Measurement of Syntactic Templates in Generated Text](https://mlsanity.com/paper/2407.00211/) (2024)
- [Robust Incremental Neural Semantic Graph Parsing](https://mlsanity.com/paper/1704.07092/) (2017)
- [A Linguistic Comparison between Human and ChatGPT-Generated Conversations](https://mlsanity.com/paper/2401.16587/) (2024)
- [Explaining Code Examples in Introductory Programming Courses: LLM vs Humans](https://mlsanity.com/paper/2403.05538/) (2023)
- [Does a Large Language Model Really Speak in Human-Like Language?](https://mlsanity.com/paper/2501.01273/) (2025)

## Built on by (most cited first, 11 of 11)

- [More Aligned, Less Diverse? Analyzing the Grammar and Lexicon of Two Generations of LLMs](https://mlsanity.com/paper/2605.06030/) (2026)
- [Word Overuse and Alignment in Large Language Models: The Influence of Learning from Human Feedback](https://mlsanity.com/paper/2508.01930/) (2025)
- [AI-Associated Lexical Shifts Across 34 Languages: Cross-Lingual Convergence and Diachronic Uptake in News Writing](https://mlsanity.com/paper/2605.25358/) (2026)
- [Surprisal reveals diversity gaps in image captioning and different scorers change the story](https://mlsanity.com/paper/2511.04754/) (2025)
- [Learn-To-Learn on Arbitrary Textual Conditioning: A Hypernetwork-Driven Meta-Gated LLM](https://mlsanity.com/paper/2605.01973/) (2026)
- [What Are LLMs Doing to Scientific Communication? Measuring Changes in Writing Practices and Reading Experience](https://mlsanity.com/paper/2605.19936/) (2026)
- [How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework](https://mlsanity.com/paper/2605.23651/) (2026)
- [Fully Automated Identification of Lexical Alignment and Preference-Stage Shifts in Large Language Models](https://mlsanity.com/paper/2606.03165/) (2026)
- [Detecting undisclosed LLM-generated content in parliamentary texts](https://mlsanity.com/paper/2606.14209/) (2026)
- [Detecting and Guiding LLM-Generated Korean Poetry with Interpretable Form-level Features](https://mlsanity.com/paper/2608.28986/) (2026)
- [Synthetic Data Characterization via Training Dynamics](https://mlsanity.com/paper/2609.39447/) (2026)

## Abstract (arXiv)

This study provides the first comprehensive comparison of New York Times-style text generated by six large language models against real, human-authored NYT writing. The comparison is based on a formal syntactic theory. We use Head-driven Phrase Structure Grammar (HPSG) to analyze the grammatical structure of the texts. We then investigate and illustrate the differences in the distributions of HPSG grammar types, revealing systematic distinctions between human and LLM-generated writing. These findings contribute to a deeper understanding of the syntactic behavior of LLMs as well as humans, within the NYT genre.