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Towards the relationship between AIGC in manuscript writing and author profiles: evidence from preprints in LLMs

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arxiv 2404.15799 v1 pith:5KXNYXS6 submitted 2024-04-24 cs.DL

classification cs.DL
keywords aigctoolswritingai-generatedlikelyacademicauthorauthors
verification ladder T0 review T1 audit T2 compute T3 formal
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AIGC tools such as ChatGPT have profoundly changed scientific research, leading to widespread attention on its use on academic writing. Leveraging preprints from large language models, this study examined the use of AIGC in manuscript writing and its correlation with author profiles. We found that: (1) since the release of ChatGPT, the likelihood of abstracts being AI-generated has gradually increased; (2) scientists from English-speaking countries are less likely to use AIGC tools for writing assistance, while those from countries with linguistic differences from English are more likely to use these tools; (3) there is weak correlation between a paper's AI-generated probability and authors' academic performance; and (4) authors who have previously published papers with high AI-generated probabilities are more likely to continue using AIGC tools. We believe that this paper provides insightful results for relevant policies and norms and in enhancing the understanding of the relationship between humans and AI.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Word Overuse and Alignment in Large Language Models: The Influence of Learning from Human Feedback

    cs.CL 2025-08 conditional novelty 6.0 of 10

    People prefer text containing the words that an instruction-tuned model uses far more than its base version, linking human feedback training to LLM word overuse.

  2. Model Misalignment and Language Change: Traces of AI-Associated Language in Unscripted Spoken English

    cs.CL 2025-08 conditional novelty 6.0 of 10

    After ChatGPT's release, science and tech podcast speakers used AI-associated words like 'surpass' and 'align' more often, while control synonyms showed no average shift.

  3. Exploring the Structure of AI-Induced Language Change in Scientific English

    cs.CL 2025-06 conditional novelty 6.0 of 10

    In PubMed abstracts, AI-associated 'spiking' words rise together with their synonyms rather than replacing them, and declining words show less systematic, more organic patterns.

  4. Human-LLM Coevolution: Evidence from Academic Writing

    cs.CL 2025-02 conditional novelty 6.0 of 10

    After ChatGPT-style words were publicly flagged in early 2024, their frequency in arXiv abstracts dropped, while other common LLM-favored words kept rising, suggesting authors are adapting their writing to avoid detection.

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