English(EN) Large Language Models Threaten Double-blind Review

大型语言模型破坏双盲同行评审

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-07 04:00

一篇新发表在arXiv上的论文详细介绍了大型语言模型（LLMs）如何对双盲同行评审过程的完整性构成重大威胁。研究人员证明，即使消除了风格和文献线索，LLMs也能比人类更有效地从匿名手稿中识别出作者。这是通过识别问题构建和研究重点中的潜在概念特征来实现的，表明需要重新评估AI增强研究中当前的匿名和公平性实践。 AI

影响 LLMs能够揭示研究论文作者身份，这使得有必要重新评估同行评审过程，以维护科学的完整性。

排序理由该集群包含一篇学术论文，详细介绍了LLMs对研究过程的新影响。 [lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该集群包含一篇学术论文，详细介绍了LLMs对研究过程的新影响。 [lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.AI TIER_1 English(EN) · Bulambo Mwendelwa Gloire, Prasenjit Mitra · 2026-08-07 04:00

大型语言模型威胁双盲评审

arXiv:2608.05157v1 Announce Type: cross Abstract: Double blind peer review serves as the scientific community primary defense against status and affiliation bias. Its effectiveness rests on the assumption that anonymized manuscripts convey scientific merit without revealing their…

本文如何被排名

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大型语言模型威胁双盲评审

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