Muñoz-Ortiz, AlbertoGómez-Rodríguez, CarlosVilares, David2024-08-292024-08-292024Muñoz-Ortiz, A., Gómez-Rodríguez, C. & Vilares, D. Contrasting Linguistic Patterns in Human and LLM-Generated News Text. Artif Intell Rev 57, 265 (2024). https://doi.org/10.1007/s10462-024-10903-2http://hdl.handle.net/2183/38751Financiado para publicación en acceso aberto: Universidade da Coruña/CISUGFinanciado para publicación en acceso aberto: CRUE-CSIC/Springer NatureThe data analyzed on this article has been obtained using the New York Times Archive API (https://developer.nytimes.com/docs/archive-product/1/overview), gathering all the available articles from the 1st of October, 2023 to the 24th of January, 2024. We released the code and the used data on: https://zenodo.org/records/11186264.[Abstract]: We conduct a quantitative analysis contrasting human-written English news text with comparable large language model (LLM) output from six different LLMs that cover three different families and four sizes in total. Our analysis spans several measurable linguistic dimensions, including morphological, syntactic, psychometric, and sociolinguistic aspects. The results reveal various measurable differences between human and AI-generated texts. Human texts exhibit more scattered sentence length distributions, more variety of vocabulary, a distinct use of dependency and constituent types, shorter constituents, and more optimized dependency distances. Humans tend to exhibit stronger negative emotions (such as fear and disgust) and less joy compared to text generated by LLMs, with the toxicity of these models increasing as their size grows. LLM outputs use more numbers, symbols and auxiliaries (suggesting objective language) than human texts, as well as more pronouns. The sexist bias prevalent in human text is also expressed by LLMs, and even magnified in all of them but one. Differences between LLMs and humans are larger than between LLMs.engAttribution 4.0 International (CC BY)http://creativecommons.org/licenses/by/3.0/es/Large language modelsComputational linguisticsMachine-generated textLinguistic biasesjournal articleopen access10.1007/s10462-024-10903-2