info: T. B. Brown et al., “Language Models are Few-Shot Learners,” 2020, doi: 10.48550/ARXIV.2005.14165. A. Radford, K. Narasimhan, T. Salimans, I. Sutskever, and others, “Improving language understanding by generative pre-training,” 2018. A. Radford et al., “Language models ar...
Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any...
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even ...
[13] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020. [14] Colin Ra...
Finetuned language models are zero-shot learners. International Conference on Learning Representations (ICLR), 2022a. Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. Emergent abilities of large ...
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Brown, T. B. et al. Language models are few-shot learners.Adv. Neural Inf. Process. Syst.33, 1877–1901 (2020). Google Scholar Cho, W. S. et al. Towards coherent and cohesive long-form text generation. inProceedings of the First Workshop on Narrative Understandinghttps://doi.org/10.18...
Language models are few-shot learners. Adv Neurl Inf Process Syst. 2020;2005:14165. Google Scholar Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov, V. Roberta: A robustly optimized bert pretraining approach. 2019; arXiv preprint arXiv:...
Language models are few-shot learners. In Advances in Neural Information Processing Systems (eds Larochelle, H. et al.) 1877–1901 (Curran Associates Inc., 2020). Google Scholar Kudo, T. & Richardson, J. SentencePiece: A simple and language independent subword tokenizer and detokenizer for ...
Language Models Are Few-Shot Learners. arXiv 2020, arXiv:2005.14165. [Google Scholar] Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; Sutskever, I. Language Models Are Unsupervised Multitask Learners; OpenAI Blog: San Francisco, CA, USA, 2019. [Google Scholar] Disclaimer/...