OpenAI 推出的 ChatGPT 到底是不是 1750 亿参数的等价大模型呢？这篇文章或许能带给你答案。

We use 2.4 million spectroscopically labelled sources from the SDSS catalogue to train an optimized random forest classifier using photometry from the Sloan Digital Sky Survey (SDSS) and Widefield Infrared Survey Explorer (WISE). We apply this machine learning model to 111 million previously unlabelled sources from the SDSS photometric catalogue without existing spectroscopic observations. Our new catalogue contains 49.7 million galaxies, 2.4 million quasars, and 59.2 million stars. We provide individual classification probabilities for each source, with 6.4 million galaxies (13%), 0.35 million quasars (14%) and 44.3 million stars (75%) having classification probabilities greater than 0.99, and 34.8 million galaxies (70%), 0.77 million quasars (32%) and 55.3 million stars (93\%) having classification probabilities greater than 0.9. We determine Precision, Recall and F1 score as a function of feature selection, including scenarios with additional external variables.