LLM use in peer review appears uneven across venues and deadline-driven: Lee et al. 2025 report detected LLM modification rates of 6.5–16.9% in AI conference reviews but no significant signal in Nature journals, while She 2026 notes that LLM-generated reviews spike as deadlines approach.
E29 reports LLM modification rates of 6.5–16.9% in AI conference peer reviews and no significant evidence of LLM-based modifications in Nature journals, and E22 states that the estimated fraction of LLM-generated reviews spikes as deadlines approach.
Written by Kimi K3 via Ollama Cloud · checked by GLM-5.3 via Ollama Cloud · 1 Oct, 07:53
Source chain
AEvery quote below was checked, without a model, to appear verbatim in its source.
- 01
“demonstrated LLM modification rates of 6.5% to 16.9% in AI conference peer reviews”
Full-text passage · no page number · evidence E29
Passage read from www.ebi.ac.uk, which may be a preprint rather than the published version.
- 02
“a similar analysis of journals in the Nature journals showed no significant evidence of LLM-based modifications”
Full-text passage · no page number · evidence E29
Passage read from www.ebi.ac.uk, which may be a preprint rather than the published version.
- 03
“fraction of LLM-generated reviews spikes as deadlines approach”
Full-text passage · p. 2 · evidence E22
of these systems, one might reasonably wonder to what extent AI-generated writing has already made its way into the scientific literature. Since individual cases of AI usage have thus far proven impossible to adjudicate, existing studies have focused on coarse grained metrics. One study that analyzed over 15 million abstracts from the biomedical literature documented the emergence of a bona fide LLM lexicon, with specific model-favored words rising at rates that cannot be explained by natural linguistic drift2. Other case reports point to the use of generative AI during peer review3, suggesting that academics find these tools both useful and embarrassing, deploying them only behind the shield of anonymity for work that never becomes part of the permanent record. A broader analysis of conference peer-review data reveals that the estimat ed fraction of LLM-generated reviews spikes as deadlines approach4, underscoring the messy practical and behavioral variables that determine where and when generative AI is used. However, these studies leave several critical questions unanswered: 1) who exactly is using generative AI?
Passage read from www.biorxiv.org, which may be a preprint rather than the published version.