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Large language models such as GPT-3 (Brown et al., 2020) can perform arbitrary tasks without undergoing fine-tuning after being prompted with only a few labeled examples.
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A call for clarity in reporting BLEU scores
Matt Post · 2018
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer · 2019
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Roberta: A robustly optimized bert pretraining approach
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Language models are unsupervised multitask learners
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Bert has a mouth, and it must speak: Bert as a markov random field language model
Alex Wang and Kyunghyun Cho · 2019
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Mikel Artetxe, Sebastian Ruder, and Dani Yogatama · 2020
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Language models are few-shot learners
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Electra: Pre-training text encoders as discriminators rather than generators
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Unsupervised cross-lingual representation learning at scale
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Metaicl: Learning to learn in context
Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi · 2021
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It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze · 2021
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2021
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Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Multilingual denoising pre-training for neural machine translation
Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, and Luke Zettlemoyer · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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GPT-NeoX: Large Scale Autoregressive Language Modeling in PyTorch, 8 2021
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GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, March 2021
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman · 2021
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The flores-101 evaluation benchmark for low-resource and multilingual machine translation
Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc’Aurelio Ranzato, Francisco Guzman, and Angela Fan · 2021
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Unsupervised neural machine translation with generative language models only
Jesse Michael Han, Igor Babuschkin, Harrison Edwards, Arvind Neelakantan, Tao Xu, Stanislas Polu, Alex Ray, Pranav Shyam, Aditya Ramesh, Alec Radford, et al · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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mT5: A massively multilingual pre-trained text-to-text transformer
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BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model
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Opt: Open pre-trained transformer language models
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