3k citations · 6.9k across the 12 of their papers we have counts for
7 papers
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…
Scaling Laws for Transfer
Danny Hernandez, Jared Kaplan, Tom Henighan +1
We study empirical scaling laws for transfer learning between distributions in an unsupervised, fine-tuning setting. When we train increasingly large neural networks from-scratch o…
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…
Language Models are Few-Shot Learners
Tom B. Brown, Benjamin Mann, Nick Ryder +28
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typicall…
Scaling Laws for Neural Language Models
Jared Kaplan, Sam McCandlish, Tom Henighan +7
We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute us…
An Empirical Model of Large-Batch Training
Sam McCandlish, Jared Kaplan, Dario Amodei +1
In an increasing number of domains it has been demonstrated that deep learning models can be trained using relatively large batch sizes without sacrificing data efficiency. However…