1.5k citations · 2.2k across the 5 of their papers we have counts for
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cs.LG2021★ 1.5k cited
Evaluating Large Language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun +55
We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex p…
cs.LG2020★ 150 cited
Scaling Laws for Autoregressive Generative Modeling
Tom Henighan, Jared Kaplan, Mor Katz +16
We identify empirical scaling laws for the cross-entropy loss in four domains: generative image modeling, video modeling, multimodal imagetext models, and mathemat…
cs.LG2017★ 424 cited
Deep Learning Scaling is Predictable, Empirically
Joel Hestness, Sharan Narang, Newsha Ardalani +6
Deep learning (DL) creates impactful advances following a virtuous recipe: model architecture search, creating large training data sets, and scaling computation. It is widely belie…