Scaling Laws for Neural Language Models, Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, Dario Amodei, 2020International Conference on Learning Representations (ICLR 2020)DOI: 10.48550/arXiv.2001.08361 - This paper introduced empirical scaling laws for language model performance.
Training Compute-Optimal Large Language Models, Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, Laurent Sifre, 2022arXiv preprint arXiv:2203.15556DOI: 10.48550/arXiv.2203.15556 - This paper presents the Chinchilla finding, revising optimal compute allocation for LLMs, suggesting a more balanced scaling of model and dataset size.
Language Models are Few-Shot Learners, Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei, 2020Advances in Neural Information Processing Systems (NeurIPS 2020)DOI: 10.48550/arXiv.2005.14165 - Presents GPT-3, demonstrating emergent few-shot capabilities and the practical application of scaling principles in large models.