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The use of positional embeddings in transformer language models is widely accepted.
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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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Positional artefacts propagate through masked language model embeddings
Ziyang Luo, Artur Kulmizev, and Xiaoxi Mao. 2021 · 2021
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Masked language modeling and the distributional hypothesis: Order word matters pre-training for little
Koustuv Sinha, Robin Jia, Dieuwke Hupkes, Joelle Pineau, Adina Williams, and Douwe Kiela. 2021 · 2021
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The Pile: An 800gb dataset of diverse text for language modeling
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Transformer language models without positional encodings still learn positional information
Adi Haviv, Ori Ram, Ofir Press, Peter Izsak, and Omer Levy. 2022 · 2022
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What language model to train if you have one million GPU hours?
Teven Le Scao, Thomas Wang, Daniel Hesslow, Lucile Saulnier, Stas Bekman, M Saiful Bari, Stella Biderman, Hady Elsahar, Jason Phang, Ofir Press, Colin Raffel, Victor Sanh, Sheng Shen, Lintang Sutawika, Jaesung Tae, Zheng Xin Yong, Julien Launay, and Iz Beltagy. 2022 · 2022
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