most citedHuman-LLM Coevolution: Evidence from Academic Writing

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cs.CL2026

Beyond Via: Analysis and Estimation of the Impact of Large Language Models in Academic Papers

Mingmeng Geng, Yuhang Dong, Thierry Poibeau

Through an analysis of arXiv papers, we report several shifts in word usage that are likely driven by large language models (LLMs) but have not previously received sufficient atten…

cs.CL2026

Markovian Generation Chains in Large Language Models

Mingmeng Geng, Amr Mohamed, Guokan Shang +2

The widespread use of large language models (LLMs) raises an important question: how do texts evolve when they are repeatedly processed by LLMs? In this paper, we define this itera…

cs.CL2025

On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?

Mingmeng Geng, Thierry Poibeau

With the widespread use of large language models (LLMs), many researchers have turned their attention to detecting text generated by them. However, there is no consistent or precis…

cs.CL2025

code_transformed: The Influence of Large Language Models on Code

Yuliang Xu, Siming Huang, Mingmeng Geng +3

Coding remains one of the most fundamental modes of interaction between humans and machines. With the rapid advancement of Large Language Models (LLMs), code generation capabilitie…

cs.CL2025

Wikipedia in the Era of LLMs: Evolution and Risks

Siming Huang, Yuliang Xu, Mingmeng Geng +2

In this paper, we present a comprehensive analysis and monitoring framework for the impact of Large Language Models (LLMs) on Wikipedia, examining the evolution of Wikipedia throug…

cs.CL20252 cited

Human-LLM Coevolution: Evidence from Academic Writing

Mingmeng Geng, Roberto Trotta

With a statistical analysis of arXiv paper abstracts, we report a marked drop in the frequency of several words previously identified as overused by ChatGPT, such as "delve", start…