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We introduce GPT-NeoX-20B, a 20 billion parameter autoregressive language model trained on the Pile, whose weights will be made freely and openly available to the public through a permissive license.
Language models are few-shot learners
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Generating long sequences with sparse transformers
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Analysing mathematical reasoning abilities of neural models
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Aran Komatsuzaki. 2019 · 1906
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Megatron-LM: Training multi-billion parameter language models using model parallelism
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Quantifying the carbon emissions of machine learning
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Compressive transformers for long-range sequence modelling
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Scaling laws for neural language models
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The Enron corpus: A new dataset for email classification research
Bryan Klimt and Yiming Yang. 2004 · 2004
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Europarl: A parallel corpus for statistical machine translation
Philipp Koehn. 2005 · 2005
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On faithfulness and factuality in abstractive summarization
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Scaling laws for autoregressive generative modeling
Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B. Brown, Prafulla Dhariwal, Scott Gray, Chris Hallacy, Benjamin Mann, Alec Radford, Aditya Ramesh, Nick Ryder, Daniel M. Ziegler, John Schulman, Dario Amodei, and Sam McCandlish. 2020 · 2010
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mT5: A massively multilingual pre-trained text-to-text transformer
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QA4MRE 2011-2013: Overview of question answering for machine reading evaluation
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IRL is hard
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The LAMBADA dataset: Word prediction requiring a broad discourse context
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
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Crowdsourcing multiple choice science questions
Johannes Welbl, Nelson F. Liu, and Matt Gardner. 2017 · 2017
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Occam’s razor is insufficient to infer the preferences of irrational agents
Stuart Armstrong and Sören Mindermann. 2018 · 2018
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Think you have solved question answering? try ARC, the AI2 Reasoning Challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
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PipeDream: Fast and efficient pipeline parallel DNN training
Aaron Harlap, Deepak Narayanan, Amar Phanishayee, Vivek Seshadri, Nikhil Devanur, Greg Ganger, and Phil Gibbons. 2018 · 2018
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Can a suit of armor conduct electricity? A new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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The parable of Predict-O-Matic
Abram Demski. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Energy and policy considerations for deep learning in NLP
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 2019
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David Vilares and Carlos Gómez-Rodríguez. 2019 · 2019
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SuperGLUE: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2019 · 2019
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Yonatan Bisk, Rowan Zellers, Ronan Le bras, Jianfeng Gao, and Yejin Choi. 2020 · 2020
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Thread: Circuits
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Shaking the foundations: delusions in sequence models for interaction and control
Pedro A. Ortega, Markus Kunesch, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Joel Veness, Jonas Buchli, Jonas Degrave, Bilal Piot, Julien Perolat, Tom Everitt, Corentin Tallec, Emilio Parisotto, Tom Erez, Yutian Chen, Scott Reed, Marcus Hutter, Nando de Freitas, and Shane Legg. 2021 · 2021
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Everyone should decide how their digital data are used — not just tech companies
Jathan Sadowski, Salomé Viljoen, and Meredith Whittaker. 2021 · 2021
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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The Pile: An 800GB dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy. 2020 · 2020
Cited alongside, same era.
LogiQA: A challenge dataset for machine reading comprehension with logical reasoning
Jian Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang. 2020 · 2020
Cited alongside, same era.
Why we need industry-independent research on tech & society
J. Nathan Matias. 2020 · 2020
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Adversarial NLI: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020 · 2020
Cited alongside, same era.
interpreting GPT: the logit lens
nostalgebraist. 2020 · 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 · 2020
Cited alongside, same era.
WinoGrande: An adversarial Winograd Schema Challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2021 · 2021
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Févry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, and Alexander M. Rush. 2021 · 2021
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Addressing privacy threats from machine learning
Mary Anne Smart. 2021 · 2021
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Visible thoughts project and bounty announcement
Nate Soares. 2021 · 2021
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RoFormer: Enhanced transformer with rotary position embedding
Jianlin Su, Yu Lu, Shengfeng Pan, Bo Wen, and Yunfeng Liu. 2021 · 2021
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ERNIE 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation
Yu Sun, Shuohuan Wang, Shikun Feng, Siyu Ding, Chao Pang, Junyuan Shang, Jiaxiang Liu, Xuyi Chen, Yanbin Zhao, Yuxiang Lu, Weixin Liu, Zhihua Wu, Weibao Gong, Jianzhong Liang, Zhizhou Shang, Peng Sun, Wei Liu, Xuan Ouyang, Dianhai Yu, Hao Tian, Hua Wu, and Haifeng Wang. 2021 · 2021
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MSP: Multi-stage prompting for making pre-trained language models better translators
Zhixing Tan, Xiangwen Zhang, Shuo Wang, and Yang Liu. 2021 · 2021
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WuDao: Pretrain the world
Jie Tang. 2021 · 2021
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Mesh-Transformer-JAX: Model-parallel implementation of transformer language model with JAX
Ben Wang. 2021 · 2021
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GPT-J-6B: A 6 billion parameter autoregressive language model
Ben Wang and Aran Komatsuzaki. 2021 · 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 · 2021
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The steep cost of capture
Meredith Whittaker. 2021 · 2021
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Wei Zeng, Xiaozhe Ren, Teng Su, Hui Wang, Yi Liao, Zhiwei Wang, Xin Jiang, ZhenZhang Yang, Kaisheng Wang, Xiaoda Zhang, Chen Li, Ziyan Gong, Yifan Yao, Xinjing Huang, Jun Wang, Jianfeng Yu, Qi Guo, Yue Yu, Yan Zhang, Jin Wang, Hengtao Tao, Dasen Yan, Zexuan Yi, Fang Peng, Fangqing Jiang, Han Zhang, Lingfeng Deng, Yehong Zhang, Zhe Lin, Chao Zhang, Shaojie Zhang, Mingyue Guo, Shanzhi Gu, Gaojun Fan, Yaowei Wang, Xuefeng Jin, Qun Liu, and Yonghong Tian. 2021 · 2021
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Counterfactual memorization in neural language models
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Adapting language models for zero-shot learning by meta-tuning on dataset and prompt collections
Ruiqi Zhong, Kristy Lee, Zheng Zhang, and Dan Klein. 2021 · 2021
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Neural language models are effective plagiarists
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PaLM: Scaling language modeling with pathways
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