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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 ...
43. Radford Alec, Wu Jeffrey, Child Rewon, Luan David, Amodei Dario, Sutskever Ilya,Language Models are Unsupervised Multitask Learners | Enhanced Reader. OpenAI Blog.1 (2019). 44. T. le Scao et al., BLOOM: A 176B-Parameter Open-Access Multilingual Language Model(2022), doi:10.48550/ar...
[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. ...
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...
Brown, T. et al. Language models are few-shot learners.Adv. Neural Inf. Process. Syst.33, 1877–1901 (2020). Google Scholar Schellaert, W. et al. Your prompt is my command: on assessing the human-centred generality of multimodal models.J. Artif. Intell. Res.77, 85–122 (2023). ...
doi:10.1038/s41523-023-00557-8PubMedGoogle ScholarCrossref 25. Brown T, Mann B, Ryder N, et al. Language Models are Few-Shot Learners. In: Advances in Neural Information Processing Systems. Published 2020. Accessed October 11, 2022. https://papers.nips.cc/paper/2020/hash/1457...
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 ...
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 ...