Language Models are Few-Shot Learners
Brown et al., 2020, Language Models are Few-Shot Learners (Advances in Neural Information Processing Systems 33 (NeurIPS 2020))
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2.1 Model and Architectures
- How does GPT-3’s architecture differ from GPT-2’s?It is the same (modified initialization, pre-normalization, byte-level BPE) except for alternating dense and locally banded sparse attention patterns, as in the Sparse Transformer. Eight sizes were trained, from 125M to 175B parameters, to test the scaling laws.
- Brown et al., 2020, Language Models are Few-Shot Learners (Advances in Neural Information Processing Systems 33 (NeurIPS 2020)), §2.1
Source
- 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 and Dario Amodei. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems 33 (NeurIPS 2020). https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html