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Large language models (LLMs) have demonstrated remarkable success as foundational models, benefiting various downstream applications through fine-tuning.
R. Egele, R. Maulik, K. Raghavan, B. Lusch, I. Guyon, and P. Balaprakash, “Autodeuq: Automated deep ensemble with uncertainty quantification,” in 2022 26th International Conference on Pattern Recognition (ICPR) . IEEE, 2022, pp. 1908–1914
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., vol. 30. Curran Associates, Inc., 2017
2017
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S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems 30 , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds. Curran Associates, Inc., 2017, pp. 4765–4774. [Online]. Available: http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf
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2018
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A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al. , “Improving language understanding by generative pre-training,” 2018
2018
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P. Balaprakash, M. Salim, T. D. Uram, V. Vishwanath, and S. M. Wild, “Deephyper: Asynchronous hyperparameter search for deep neural networks,” in 2018 IEEE 25th international conference on high performance computing (HiPC) . IEEE, 2018, pp. 42–51
2018
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Y. Huang, Y. Cheng, A. Bapna, O. Firat, D. Chen, M. Chen, H. Lee, J. Ngiam, Q. V. Le, Y. Wu et al. , “Gpipe: Efficient training of giant neural networks using pipeline parallelism,” Advances in neural information processing systems , vol. 32, 2019
2019
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D. Narayanan, A. Harlap, A. Phanishayee, V. Seshadri, N. R. Devanur, G. R. Ganger, P. B. Gibbons, and M. Zaharia, “Pipedream: Generalized pipeline parallelism for dnn training,” in Proceedings of the 27th ACM Symposium on Operating Systems Principles , 2019, pp. 1–15
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2020
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T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
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S. Rajbhandari, J. Rasley, O. Ruwase, and Y. He, “Zero: Memory optimizations toward training trillion parameter models,” in SC20: International Conference for High Performance Computing, Networking, Storage and Analysis . IEEE, 2020, pp. 1–16
2020
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R. Maulik, R. Egele, B. Lusch, and P. Balaprakash, “Recurrent neural network architecture search for geophysical emulation,” in SC20: International Conference for High Performance Computing, Networking, Storage and Analysis . IEEE, 2020, pp. 1–14
2020
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D. Narayanan, M. Shoeybi, J. Casper, P. LeGresley, M. Patwary, V. Korthikanti, D. Vainbrand, P. Kashinkunti, J. Bernauer, B. Catanzaro et al. , “Efficient large-scale language model training on gpu clusters using megatron-lm,” in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis , 2021, pp. 1–15
T. Dao, D. Fu, S. Ermon, A. Rudra, and C. Ré, “Flashattention: Fast and memory-efficient exact attention with io-awareness,” Advances in Neural Information Processing Systems , vol. 35, pp. 16 344–16 359, 2022
2022
Later among the works it cites.
J. Yin, S. Dash, F. Wang, and M. Shankar, “Forge: Pre-training open foundation models for science,” in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis , 2023, pp. 1–13
2023
Closest in time.
T. Computer, “Redpajama: an open dataset for training large language models,” 2023. [Online]. Available: https://github.com/togethercomputer/RedPajama-Data
2023
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L. Soldaini, R. Kinney, A. Bhagia, D. Schwenk, D. Atkinson, R. Authur, K. Chandu, J. Dumas, L. Lucy, X. Lyu, I. Magnusson, A. Naik, C. Nam, M. E. Peters, A. Ravichander, Z. Shen, E. Strubell, N. Subramani, O. Tafjord, E. P. Walsh, H. Hajishirzi, N. A. Smith, L. Zettlemoyer, I. Beltagy, D. Groeneveld, J. Dodge, and K. Lo, “Dolma: An Open Corpus of 3 Trillion Tokens for Language Model Pretraining Research,” Allen Institute for AI, Tech. Rep., 2023, released under ImpACT License as Medium Risk artifact, https://github.com/allenai/dolma
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2021
Cited alongside, same era.
S. Li and T. Hoefler, “Chimera: efficiently training large-scale neural networks with bidirectional pipelines,” in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis , 2021, pp. 1–14
2021
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
https://github.com/microsoft/Megatron-DeepSpeed
Cited in the paper.
https://github.com/NVIDIA/Megatron-LM
Cited in the paper.
https://github.com/ROCmSoftwarePlatform
Cited in the paper.
2023
Closest in time.
Soldaini, Luca and Lo, Kyle and Kinney, Rodney and Naik, Aakanksha and Ravichander, Abhilasha and Bhagia, Akshita and Groeneveld, Dirk and Schwenk, Dustin and Magnusson, Ian and Chandu, Khyathi, “The Dolma Toolkit,” 2023, Apache 2.0 License, Version 0.9.0
2023
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J. Yin, S. Dash, J. Gounley, F. Wang, and G. Tourassi, “Evaluation of pre-training large language models on leadership-class supercomputers,” The Journal of Supercomputing , pp. 1–22, 2023
2023
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2023
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2023
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R. Egelé, I. Guyon, V. Vishwanath, and P. Balaprakash, “Asynchronous decentralized bayesian optimization for large scale hyperparameter optimization,” in 2023 IEEE 19th International Conference on e-Science (e-Science) . IEEE, 2023, pp. 1–10
2023
Closest in time.