ML Papers with hundreds of authors should just collapse down to the organization response instead of listing every author [D](reddit.com)

I've had this feeling for a while when reading the citation pages of ML papers and had to continuously scroll down for like 2+ pages because a citation with hundreds of authors, such as the Llama herd of model paper: https://arxiv.org/abs/2407.21783 (no endorsement of this paper btw). Now Chinese papers are catching up to this trend and let me remind you they have like 20-40 times the ML researchers compared to the US alone, which has resulted in some incredible sights https://arxiv.org/abs/2501.12948 https://arxiv.org/abs/2605.26494 (again, no endorsement, just examples). Listing authors is a practice that carries utility, because the authors have real-world reputation to uphold, which give credence to the correctness or depth of the paper. I mean if Geoff Hinton published a paper tomorrow I'd take that more seriously than a paper published by 400 non-Geoff Hintons. Also I got the wind from a friend who works at one of these companies (Yes a Chinese company) that people who are almost completely irrelevant to the contributions of these papers are being listed as authors. For example, a person who was responsible for generating an Excel bar-chart from a row of data points [1.2, 2.4, 5.6, ...] was put on the paper. Another person did some double-checking of the reference page to make sure the format is month-year and not year-month. And apparently huge amount of office politics are involved as to who goes on the list of authors along with silly fights over the ordering of the authorships. It's just all completely meaningless at this point because you can't make sound attributions, it's all he said she said. Just list the organization that did the research as opposed to listing all the individual contributors (when it exceeds like 20+ people), it's not that hard. submitted by /u/NeighborhoodFatCat [link] [Kommentare]