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Length extrapolation permits training a transformer language model on short sequences that preserves perplexities when tested on substantially longer sequences.
Tener: adapting transformer encoder for named entity recognition
Hang Yan, Bocao Deng, Xiaonan Li, and Xipeng Qiu. 2019 · 1911
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GMAT: global memory augmentation for transformers
Ankit Gupta and Jonathan Berant. 2020 · 2006
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Big bird: Transformers for longer sequences
Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontañón, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, and Amr Ahmed. 2020 · 2007
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Recurrent neural network based language model
Tomas Mikolov, Martin Karafiát, Lukas Burget, Jan Cernockỳ, and Sanjeev Khudanpur. 2010 · 2010
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Context dependent recurrent neural network language model
Tomas Mikolov and Geoffrey Zweig. 2012 · 2012
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Recurrent neural network regularization
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. 2014 · 2014
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016 · 2016
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Wenjie Luo, Yujia Li, Raquel Urtasun, and Richard Zemel. 2016 · 2016
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Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. 2016 · 2016
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Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun. 2016 · 2016
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Dilated recurrent neural networks
Shiyu Chang, Yang Zhang, Wei Han, Mo Yu, Xiaoxiao Guo, Wei Tan, Xiaodong Cui, Michael Witbrock, Mark A Hasegawa-Johnson, and Thomas S Huang. 2017 · 2017
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Deformable convolutional networks
Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Computing receptive fields of convolutional neural networks
Andre Araujo, Wade Norris, and Jack Sim. 2019 · 2019
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ETC: Encoding long and structured inputs in transformers
GPT-NeoX: Large Scale Autoregressive Language Modeling in PyTorch
Alex Andonian, Quentin Anthony, Stella Biderman, Sid Black, Preetham Gali, Leo Gao, Eric Hallahan, Josh Levy-Kramer, Connor Leahy, Lucas Nestler, Kip Parker, Michael Pieler, Shivanshu Purohit, Tri Songz, Wang Phil, and Samuel Weinbach. 2021 · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021 · 2021
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Do vision transformers see like convolutional neural networks?
Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, and Alexey Dosovitskiy. 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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Exploring length generalization in large language models
Cem Anil, Yuhuai Wu, Anders Johan Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay Venkatesh Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, and Behnam Neyshabur. 2022 · 2022
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Joshua Ainslie, Santiago Ontanon, Chris Alberti, Vaclav Cvicek, Zachary Fisher, Philip Pham, Anirudh Ravula, Sumit Sanghai, Qifan Wang, and Li Yang. 2020 · 2020
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Longformer: The long-document transformer
Iz Beltagy, Matthew E. Peters, and Arman Cohan. 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
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Kerple: Kernelized relative positional embedding for length extrapolation
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Train short, test long: Attention with linear biases enables input length extrapolation
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Bloom: A 176b-parameter open-access multilingual language model
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