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The training and inference of large language models (LLMs) are together a costly process that transports knowledge from raw data to meaningful computation.
The impaired learning of semantic knowledge following bilateral medial temporal-lobe resection
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The magical number 4 in short-term memory: A reconsideration of mental storage capacity
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What’s new with the amnesic patient H.M.?
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Failure to acquire new semantic knowledge in patients with large medial temporal lobe lesions
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Approximate nearest neighbor search by residual vector quantization
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Unsupervised cross-lingual representation learning at scale
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Transformer-xl: Attentive language models beyond a fixed-length context, 2019
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, and Ruslan Salakhutdinov · 2019
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Augmenting self-attention with persistent memory, 2019
Sainbayar Sukhbaatar, Edouard Grave, Guillaume Lample, Herve Jegou, and Armand Joulin · 2019
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Depth-adaptive transformer, 2020
Maha Elbayad, Jiatao Gu, Edouard Grave, and Michael Auli · 2020
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Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang · 2020
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Tinybert: Distilling bert for natural language understanding, 2020
Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu · 2020
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Scaling laws for neural language models, 2020
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Colbert: Efficient and effective passage search via contextualized late interaction over bert, 2020
Omar Khattab and Matei Zaharia · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He · 2020
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Megatron-lm: Training multi-billion parameter language models using model parallelism, 2020
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro · 2020
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Jec-qa: A legal-domain question answering dataset
Haoxi Zhong, Chaojun Xiao, Cunchao Tu, Tianyang Zhang, Zhiyuan Liu, and Maosong Sun · 2020
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Knowledge neurons in pretrained transformers
Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei · 2021
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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 · 2021
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Transformer feed-forward layers are key-value memories, 2021
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy · 2021
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E.R. Kandel, J.D. Koester, S.H. Mack, and S.A. Siegelbaum · 2021
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Phase diagram for two-layer relu neural networks at infinite-width limit
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Webgpt: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al · 2021
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Efficient large-scale language model training on gpu clusters using megatron-lm, 2021
Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Anand Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, and Matei Zaharia · 2021
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Scaling language models: Methods, analysis & insights from training gopher
Jack W Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al · 2021
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Gail Weiss, Yoav Goldberg, and Eran Yahav · 2021
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Memorizing transformers
Yuhuai Wu, Markus Norman Rabe, DeLesley Hutchins, and Christian Szegedy · 2021
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https://github.com/microsoft/Megatron-DeepSpeed , 2022
Megatron-DeepSpeed · 2022
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Improving language models by retrieving from trillions of tokens
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Palm: Scaling language modeling with pathways, 2022
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Toy models of superposition
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
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Formal language recognition by hard attention transformers: Perspectives from circuit complexity
Yiding Hao, Dana Angluin, and Robert Frank · 2022
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Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
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Vijay Korthikanti, Jared Casper, Sangkug Lym, Lawrence McAfee, Michael Andersch, Mohammad Shoeybi, and Bryan Catanzaro · 2022
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TruthfulQA: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2022
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Estimating the carbon footprint of bloom, a 176b parameter language model, 2022
Alexandra Sasha Luccioni, Sylvain Viguier, and Anne-Laure Ligozat · 2022
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In-context learning and induction heads, 2022
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Training language models to follow instructions with human feedback
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Grokking: Generalization beyond overfitting on small algorithmic datasets, 2022
Alethea Power, Yuri Burda, Harri Edwards, Igor Babuschkin, and Vedant Misra · 2022
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Parallel context windows for large language models
Nir Ratner, Yoav Levine, Yonatan Belinkov, Ori Ram, Inbal Magar, Omri Abend, Ehud Karpas, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham · 2022
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Interpretability in the wild: a circuit for indirect object identification in gpt-2 small
Kevin Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, and Jacob Steinhardt · 2022
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Emergent abilities of large language models, 2022
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Tensor programs v: Tuning large neural networks via zero-shot hyperparameter transfer
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A mathematical framework for learning probability distributions
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Shall we pretrain autoregressive language models with retrieval? A comprehensive study
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Guan Wang, Sijie Cheng, Xianyuan Zhan, Xiangang Li, Sen Song, and Yang Liu · 2023
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Label words are anchors: An information flow perspective for understanding in-context learning
Lean Wang, Lei Li, Damai Dai, Deli Chen, Hao Zhou, Fandong Meng, Jie Zhou, and Xu Sun · 2023
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Adan: Adaptive nesterov momentum algorithm for faster optimizing deep models, 2023
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Baichuan 2: Open large-scale language models, 2023
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Metamath: Bootstrap your own mathematical questions for large language models
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