Kobak et al. 2025 argue their method is conceptually stronger than prior detection work because it detects LLM fingerprints directly from published abstracts rather than relying on potentially biased ground-truth datasets, allowing them to conclude that LLMs' impact on scientific writing surpasses even that of the COVID pandemic.
E3 states the conceptual advantage of detecting LLM fingerprints directly from published abstracts instead of relying on potentially biased ground-truth datasets, and E2 states the conclusion that LLMs' impact on scientific writing surpasses that of the COVID pandemic.
Written by Kimi K3 via Ollama Cloud · 30 Sept, 22:48
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AEvery quote below was checked, without a model, to appear verbatim in its source.
- 01
“our analysis avoids this limitation by detecting emerging LLM fingerprints directly from published abstracts”
Full-text passage · no page number · evidence E3
Passage read from www.ebi.ac.uk, which may be a preprint rather than the published version.
- 02
“We show that LLMs have had an unprecedented impact on scientific writing in biomedical research, surpassing the effect of major world events such as the COVID pandemic”
Full-text passage · no page number · evidence E2
Passage read from www.ebi.ac.uk, which may be a preprint rather than the published version.