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We introduce the Autoregressive Block-Based Iterative Encoder (AbbIE), a novel recursive generalization of the encoder-only Transformer architecture, which achieves better perplexity than a standard Transformer and allows for the dynamic scaling of compute resources at test time.
Hellaswag: Can a machine really finish your sentence?, 2019
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Root mean square layer normalization, 2019
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Scaling laws for neural language models
J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei · 2001
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D. Paperno, G. Kruszewski, A. Lazaridou, Q. N. Pham, R. Bernardi, S. Pezzelle, M. Baroni, G. Boleda, and R. Fernández · 2016
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S. Elfwing, E. Uchibe, and K. Doya · 2017
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Attention is all you need
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P. Clark, I. Cowhey, O. Etzioni, T. Khot, A. Sabharwal, C. Schoenick, and O. Tafjord · 2018
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S. McCandlish, J. Kaplan, D. Amodei, and O. D. Team · 2018
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Universal transformers
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Commonsenseqa: A question answering challenge targeting commonsense knowledge, 2019
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Highly accurate protein structure prediction with AlphaFold
J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko, A. Bridgland, C. Meyer, S. A. A. Kohl, A. J. Ballard, A. Cowie, B. Romera-Paredes, S. Nikolov, R. Jain, J. Adler, T. Back, S. Petersen, D. Reiman, E. Clancy, M. Zielinski, M. Steinegger, M. Pacholska, T. Berghammer, S. Bodenstein, D. Silver, O. Vinyals, A. W. Senior, K. Kavukcuoglu, P. Kohli, and D. Hassabis · 2021
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Show your work: Scratchpads for intermediate computation with language models
M. I. Nye, A. J. Andreassen, G. Gur-Ari, H. Michalewski, J. Austin, D. Bieber, D. Dohan, A. Lewkowycz, M. Bosma, D. Luan, C. Sutton, and A. Odena · 2021
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Path independent equilibrium models can better exploit test-time computation
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Glam: Efficient scaling of language models with mixture-of-experts, 2022
N. Du, Y. Huang, A. M. Dai, S. Tong, D. Lepikhin, Y. Xu, M. Krikun, Y. Zhou, A. W. Yu, O. Firat, B. Zoph, L. Fedus, M. Bosma, Z. Zhou, T. Wang, Y. E. Wang, K. Webster, M. Pellat, K. Robinson, K. Meier-Hellstern, T. Duke, L. Dixon, K. Zhang, Q. V. Le, Y. Wu, Z. Chen, and C. Cui · 2022
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Softmax linear units
N. Elhage, T. Hume, C. Olsson, N. Nanda, T. Henighan, S. Johnston, S. ElShowk, N. Joseph, N. DasSarma, B. Mann, D. Hernandez, A. Askell, K. Ndousse, A. Jones, D. Drain, A. Chen, Y. Bai, D. Ganguli, L. Lovitt, Z. Hatfield-Dodds, J. Kernion, T. Conerly, S. Kravec, S. Fort, S. Kadavath, J. Jacobson, E. Tran-Johnson, J. Kaplan, J. Clark, T. Brown, S. McCandlish, D. Amodei, and C. Olah · 2022
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
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Training compute-optimal large language models
J. Hoffmann, S. Borgeaud, A. Mensch, E. Buchatskaya, T. Cai, E. Rutherford, D. de Las Casas, L. A. Hendricks, J. Welbl, A. Clark, T. Hennigan, E. Noland, K. Millican, G. van den Driessche, B. Damoc, A. Guy, S. Osindero, K. Simonyan, E. Elsen, J. W. Rae, O. Vinyals, and L. Sifre · 2022
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GQA: Training generalized multi-query transformer models from multi-head checkpoints
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M. Elhoushi, A. Shrivastava, D. Liskovich, B. Hosmer, B. Wasti, L. Lai, A. Mahmoud, B. Acun, S. Agarwal, A. Roman, A. A. Aly, B. Chen, and C. Wu · 2024
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The llama 3 herd of models, 2024
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