大语言模型对双盲评审构成威胁

中文摘要 AI 辅助

双盲同行评审是科学界抵御地位与机构偏见的主要手段，其有效性基于匿名手稿能传递科学价值且不暴露作者身份的假设。虽然作者身份常可通过引用网络或文体标记被恢复，但我们表明，在大语言模型（LLMs）存在时，这一假设愈发脆弱。仅利用模型训练后发表论文的标题与摘要，我们发现LLMs比人类更高效地破坏匿名性，置信度会集中于来自5名领域专家候选池的一小部分合理作者。即使排除文体和书目线索，该漏洞依然存在，表明问题框架与研究焦点的稳定模式可作为作者身份的潜在概念特征。综上，这些发现显示双盲评审易受自动语义推理影响，需重新评估在AI增强的研究生态系统中如何维护匿名性与公平性。

英文摘要

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 authors. While authorship can often be recovered using citation networks or stylistic markers, we show that this assumption is increasingly fragile in the presence of large language models (LLMs). Using only titles and abstracts from papers published after model training, we find that LLMs collapse anonymity more efficiently than humans, with belief concentrating onto a small subset of plausible authors drawn from pools of five domain expert candidates. This vulnerability persists even when stylistic and bibliographic cues are excluded, indicating that stable patterns in problem framing and research focus function as latent conceptual signatures of authorship. Together, these findings indicate that double blind review is vulnerable to automated semantic inference, necessitating a revaluation of how anonymity and fairness are maintained in an AI augmented research ecosystem.

发表机构

• Carnegie Mellon University Africa（卡内基梅隆大学非洲分校）

机构由 AI 辅助整理，请以论文原文为准。