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– https://t.co/H5sqnNcPYr
Title: "Large Language Model Turnover Undermines Screening for Artificial Intelligence-Assisted Scientific Writing"
Tokyo Metropolitan University paper finds model choice affects detection of AI-rewritten abstractsMachine translation
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A new paper from Tokyo Metropolitan University reports that Pangram’s detection of AI-rewritten scientific abstracts varies strongly by the model used: it caught 93.5% of GPT-5 rewrites but missed 79.8% of Meta’s Muse-Glimmer rewrites. The material also says it flagged 1 of 5,000 human abstracts.
Pangram, the AI-text detector, missed 79.8% of scientific abstracts rewritten by Meta's Muse-Glimmer, while flagging just 1 of 5,000 human abstracts.
In a new paper from Tokyo Metropolitan University, reseaerchers find the share of AI-rewritten abstracts that Pangram misses depends strongly on the LLM version
Shows that it caught 93.5% of GPT-5 rewrites but missed 79.8% from another new model. Its miss rate depended mostly on which model did the rewriting.
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