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Pretraining data of large language models composes multiple domains (e.g., web texts, academic papers, codes), whose mixture proportions crucially impact the competence of outcome models.
On the uniform convergence of relative frequencies of events to their probabilities
VN Vapnik and A Ya Chervonenkis · 1971
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A theory of the learnable
Leslie G Valiant · 1984
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Quantifying inductive bias: Ai learning algorithms and valiant’s learning framework
David Haussler · 1988
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Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
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The design of experiments: statistical principles for practical applications
Roger Mead · 1990
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Four types of learning curves
Shun-ichi Amari, Naotake Fujita, and Shigeru Shinomoto · 1992
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Improving regressors using boosting techniques
Harris Drucker · 1997
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Catastrophic forgetting in connectionist networks
Robert M French · 1999
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Pattern recognition and machine learning
Christopher Bishop · 2006
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Deep learning scaling is predictable, empirically
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Improving language understanding with unsupervised learning
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
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Ccnet: Extracting high quality monolingual datasets from web crawl data
Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, and Edouard Grave · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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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, et al · 2020
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Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A Smith · 2020
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Scaling laws for autoregressive generative modeling
Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B Brown, Prafulla Dhariwal, Scott Gray, et al · 2020
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Scaling laws for neural language models
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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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
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Explaining neural scaling laws
Yasaman Bahri, Ethan Dyer, Jared Kaplan, Jaehoon Lee, and Utkarsh Sharma · 2021
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A primer on pretrained multilingual language models
Sumanth Doddapaneni, Gowtham Ramesh, Mitesh M Khapra, Anoop Kunchukuttan, and Pratyush Kumar · 2021
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Model performance scaling with multiple data sources
Tatsunori Hashimoto · 2021
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Danny Hernandez, Jared Kaplan, Tom Henighan, and Sam McCandlish · 2021
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Marcus Hutter · 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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Revisiting neural scaling laws in language and vision
Ibrahim M Alabdulmohsin, Behnam Neyshabur, and Xiaohua Zhai · 2022
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OpenAI · 2023
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Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Alessandro Cappelli, Hamza Alobeidli, Baptiste Pannier, Ebtesam Almazrouei, and Julien Launay · 2023
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Internlm: A multilingual language model with progressively enhanced capabilities, 2023
InternLM Team · 2023
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Effective long-context scaling of foundation models
Wenhan Xiong, Jingyu Liu, Igor Molybog, Hejia Zhang, Prajjwal Bhargava, Rui Hou, Louis Martin, Rashi Rungta, Karthik Abinav Sankararaman, Barlas Oguz, et al · 2023
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Ethan Caballero, Kshitij Gupta, Irina Rish, and David Krueger · 2022
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Glam: Efficient scaling of language models with mixture-of-experts
Nan Du, Yanping Huang, Andrew M Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, et al · 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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Sparse upcycling: Training mixture-of-experts from dense checkpoints
Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, Carlos Riquelme Ruiz, Basil Mustafa, Joshua Ainslie, Yi Tay, Mostafa Dehghani, and Neil Houlsby · 2022
Cited alongside, same era.
Prioritized training on points that are learnable, worth learning, and not yet learnt
Sören Mindermann, Jan M Brauner, Muhammed T Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N Gomez, Adrien Morisot, Sebastian Farquhar, et al · 2022
Cited alongside, same era.
Galactica: A large language model for science
Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic · 2022
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Efficient online data mixing for language model pre-training
Alon Albalak, Liangming Pan, Colin Raffel, and William Yang Wang · 2023
Cited alongside, same era.
Kun Zhou, Yutao Zhu, Zhipeng Chen, Wentong Chen, Wayne Xin Zhao, Xu Chen, Yankai Lin, Ji-Rong Wen, and Jiawei Han · 2023
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Chinchilla scaling: A replication attempt
Tamay Besiroglu, Ege Erdil, Matthew Barnett, and Josh You · 2024
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Deepseek llm: Scaling open-source language models with longtermism
Xiao Bi, Deli Chen, Guanting Chen, Shanhuang Chen, Damai Dai, Chengqi Deng, Honghui Ding, Kai Dong, Qiushi Du, Zhe Fu, et al · 2024
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Doge: Domain reweighting with generalization estimation, 2024
Simin Fan, Matteo Pagliardini, and Martin Jaggi · 2024
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Scaling laws for data filtering–data curation cannot be compute agnostic
Sachin Goyal, Pratyush Maini, Zachary C Lipton, Aditi Raghunathan, and J Zico Kolter · 2024
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Towards optimal learning of language models
Yuxian Gu, Li Dong, Yaru Hao, Qingxiu Dong, Minlie Huang, and Furu Wei · 2024
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Sample relationship from learning dynamics matters for generalisation
Shangmin Guo, Yi Ren, Stefano V Albrecht, and Kenny Smith · 2024
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Autoscale: Automatic prediction of compute-optimal data composition for training llms
Feiyang Kang, Yifan Sun, Bingbing Wen, Si Chen, Dawn Song, Rafid Mahmood, and Ruoxi Jia · 2024
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Regmix: Data mixture as regression for language model pre-training
Qian Liu, Xiaosen Zheng, Niklas Muennighoff, Guangtao Zeng, Longxu Dou, Tianyu Pang, Jing Jiang, and Min Lin · 2024
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The quantization model of neural scaling
Eric Michaud, Ziming Liu, Uzay Girit, and Max Tegmark · 2024
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Scaling data-constrained language models
Niklas Muennighoff, Alexander Rush, Boaz Barak, Teven Le Scao, Nouamane Tazi, Aleksandra Piktus, Sampo Pyysalo, Thomas Wolf, and Colin A Raffel · 2024
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Resolving discrepancies in compute-optimal scaling of language models
Tomer Porian, Mitchell Wortsman, Jenia Jitsev, Ludwig Schmidt, and Yair Carmon · 2024
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Balanced data sampling for language model training with clustering
Yunfan Shao, Linyang Li, Zhaoye Fei, Hang Yan, Dahua Lin, and Xipeng Qiu · 2024
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Unraveling the mystery of scaling laws: Part i
Hui Su, Zhi Tian, Xiaoyu Shen, and Xunliang Cai · 2024
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Moss: An open conversational large language model
Tianxiang Sun, Xiaotian Zhang, Zhengfu He, Peng Li, Qinyuan Cheng, Xiangyang Liu, Hang Yan, Yunfan Shao, Qiong Tang, Shiduo Zhang, Xingjian Zhao, Ke Chen, Yining Zheng, Zhejian Zhou, Ruixiao Li, Jun Zhan, Yunhua Zhou, Linyang Li, Xiaogui Yang, Lingling Wu, Zhangyue Yin, Xuanjing Huang, Yu-Gang Jiang, and Xipeng Qiu · 2024
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Scaling laws literature review, 2023
Pablo Villalobos · 2024
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Anygpt: Unified multimodal llm with discrete sequence modeling, 2024
Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yugang Jiang, and Xipeng Qiu · 2024
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Lima: Less is more for alignment
Chunting Zhou, Pengfei Liu, Puxin Xu, Srinivasan Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, et al · 2024
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