LLM use has also entered peer review, not just manuscript writing: Lee et al. 2025 report corpus-level estimates of LLM-modified AI conference reviews, and She 2026 notes that such usage spikes as deadlines approach, revealing behavioral pressures behind adoption.
Lee et al. report corpus-level LLM modification rates of 6.5–16.9% in AI conference peer reviews (via Liang et al.), and She notes the estimated fraction of LLM-generated reviews spikes as deadlines approach, underscoring behavioral variables.
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“demonstrated LLM modification rates of 6.5% to 16.9% in AI conference peer reviews”
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Passage read from www.ebi.ac.uk, which may be a preprint rather than the published version.
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“fraction of LLM-generated reviews spikes as deadlines approach”
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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.