Beyond Via: Analysis and Estimation of the Impact of Large Language Models in Academic Papers
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866912999357284352 |
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| author | Geng, Mingmeng Dong, Yuhang Poibeau, Thierry |
| author_facet | Geng, Mingmeng Dong, Yuhang Poibeau, Thierry |
| contents | 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 attention, such as the increased frequency of "beyond" and "via" in titles and the decreased frequency of "the" and "of" in abstracts. Due to the similarities among different LLMs, experiments show that current classifiers struggle to accurately determine which specific model generated a given text in multi-class classification tasks. Meanwhile, variations across LLMs also result in evolving patterns of word usage in academic papers. By adopting a direct and highly interpretable linear approach and accounting for differences between models and prompts, we quantitatively assess these effects and show that real-world LLM usage is heterogeneous and dynamic. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25638 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Via: Analysis and Estimation of the Impact of Large Language Models in Academic Papers Geng, Mingmeng Dong, Yuhang Poibeau, Thierry Computation and Language Artificial Intelligence Computers and Society Digital Libraries Machine Learning 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 attention, such as the increased frequency of "beyond" and "via" in titles and the decreased frequency of "the" and "of" in abstracts. Due to the similarities among different LLMs, experiments show that current classifiers struggle to accurately determine which specific model generated a given text in multi-class classification tasks. Meanwhile, variations across LLMs also result in evolving patterns of word usage in academic papers. By adopting a direct and highly interpretable linear approach and accounting for differences between models and prompts, we quantitatively assess these effects and show that real-world LLM usage is heterogeneous and dynamic. |
| title | Beyond Via: Analysis and Estimation of the Impact of Large Language Models in Academic Papers |
| topic | Computation and Language Artificial Intelligence Computers and Society Digital Libraries Machine Learning |
| url | https://arxiv.org/abs/2603.25638 |