审稿人称三篇 ICLR 投稿疑似 Claude 批量生成且思路雷同
Weird pattern in the ICLR papers I was assigned to review [D]
一位 ICLR 审稿人表示自己分到的 3 篇投稿都是疑似 Claude 生成的劣质论文，语言糟糕到几乎无法阅读。三篇论文不仅核心方法相同，都从比较两个预测器优劣出发，利用误差或分数之差中共享项相消来推导更紧的界，而且引入方式、文风和附加证明与实验也高度相似。作者因此怀疑存在某个自动研究系统在批量产出这类论文，并询问是否有人见过类似情况。
I was assigned 3 ICLR papers to review. I generally consider myself a pretty nice reviewer but...all 3 of them are slop. Unfiltered Claude slop. The language is so awful it is almost impossible to read. Unfortunately, I imagine this is common among "top" conference submissions now.
The reason I am posting here is because I noticed a strange similarity between all three papers. They all use very similar methods. They all start with the premise of deciding which of two predictors is better, and say that estimating predictor errors or scores individually can be difficult. So instead, they all propose to look at the difference between two errors or scores. *In all three cases*, when you look at the difference, some troublesome shared term cancels out, allowing you to derive a tighter bound on the difference. The authors then build a whole elaborate theoretical framework around this observation.
I know that paired comparisons and cancellation are standard techniques, but I don't think they are *so* common that I should get 3 separate submissions built around that same idea. Also, not only do all 3 submissions share the same core idea, but they all introduce it in the same way, use the same Claude-sounding prose, and include tons of additional proofs and experiments in a pretty similar way.
The coincidence is weird enough that I'm wondering if there is an autoresearch setup somewhere that has been spitting out papers of this sort. Has anyone else seen this idea as well?
submitted by /u/WhiteBear2018
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来源：r/MachineLearning · reddit.com