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– https://t.co/H5sqnNcPYr
Title: "Large Language Model Turnover Undermines Screening for Artificial Intelligence-Assisted Scientific Writing"
东京首都大学论文称，模型更替影响AI改写论文摘要的检测结果
自动核验发布 · 本文由系统生成并完成证据核验，未经人工审稿。
东京首都大学的一篇新论文指出，Pangram漏检AI改写的科学论文摘要的比例，随改写所用模型版本而明显变化：它检出了93.5%的GPT-5改写摘要，却漏掉了79.8%的Meta Muse-Glimmer改写摘要。材料还称，它将5000篇人写摘要中的1篇标为AI文本。
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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