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Scale has become a main ingredient in obtaining strong machine learning models.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 1910
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A method of solving a convex programming problem with convergence rate o (1/k** 2)
Nesterov, Y · 1983
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Acceleration of stochastic approximation by averaging
Polyak, B. T. and Juditsky, A. B · 1992
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Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D · 2001
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Glu variants improve transformer
Shazeer, N · 2002
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Sgdr: Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2016
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
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Micikevicius, P., Narang, S., Alben, J., Diamos, G., Elsen, E., Garcia, D., Ginsburg, B., Houston, M., Kuchaiev, O., Venkatesh, G., et al · 2017
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Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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Don’t decay the learning rate, increase the batch size
Smith, S. L., Kindermans, P.-J., Ying, C., and Le, Q. V · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Clark, P., Cowhey, I., Etzioni, O., Khot, T., Sabharwal, A., Schoenick, C., and Tafjord, O · 2018
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Averaging weights leads to wider optima and better generalization
Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., and Wilson, A. G · 2018
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Mihaylov, T., Clark, P., Khot, T., and Sabharwal, A · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al · 2018
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
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Socialiqa: Commonsense reasoning about social interactions
Sap, M., Rashkin, H., Chen, D., LeBras, R., and Choi, Y · 2019
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CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
Talmor, A., Herzig, J., Lourie, N., and Berant, J · 2019
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HellaSwag: Can a machine really finish your sentence?
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., and Choi, Y · 2019
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Piqa: Reasoning about physical commonsense in natural language
Bisk, Y., Zellers, R., Bras, R. L., Gao, J., and Choi, Y · 2020
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., Presser, S., and Leahy, C · 2020
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Root mean square layer normalization
Zhang, B. and Sennrich, R · 2020
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Measuring Massive Multitask Language Understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2021
Cited alongside, same era.
Scaling language models: Methods, analysis & insights from training gopher
Rae, J. W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, F., Aslanides, J., Henderson, S., Ring, R., Young, S., et al · 2021
Cited alongside, same era.
Winogrande: An adversarial winograd schema challenge at scale
Sakaguchi, K., Bras, R. L., Bhagavatula, C., and Choi, Y · 2021
Cited alongside, same era.
Scale efficiently: Insights from pre-training and fine-tuning transformers
Tay, Y., Dehghani, M., Rao, J., Fedus, W., Abnar, S., Chung, H. W., Narang, S., Yogatama, D., Vaswani, A., and Metzler, D · 2021
Cited alongside, same era.
Flashattention: Fast and memory-efficient exact attention with io-awareness
Dao, T., Fu, D., Ermon, S., Rudra, A., and Ré, C · 2022
Early weight averaging meets high learning rates for llm pre-training
Sanyal, S., Neerkaje, A., Kaddour, J., Kumar, A., and Sanghavi, S · 2023
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Beyond chinchilla-optimal: Accounting for inference in language model scaling laws
Sardana, N. and Frankle, J · 2023
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SlimPajama: A 627B token cleaned and deduplicated version of RedPajama
Soboleva, D., Al-Khateeb, F., Myers, R., Steeves, J. R., Hestness, J., and Dey, N · 2023
Later among the works it cites.
Gemini: a family of highly capable multimodal models
Team, G., Anil, R., Borgeaud, S., Wu, Y., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., et al · 2023
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Deepseek llm: Scaling open-source language models with longtermism
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Cited alongside, same era.
Training compute-optimal large language models
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. d. L., Hendricks, L. A., Welbl, J., Clark, A., et al · 2022
Cited alongside, same era.
Stop wasting my time! saving days of imagenet and bert training with latest weight averaging
Kaddour, J · 2022
Cited alongside, same era.
Emergent abilities of large language models
Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., Chi, E. H., Hashimoto, T., Vinyals, O., Liang, P., Dean, J., and Fedus, W · 2022
Cited alongside, same era.
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Wortsman, M., Ilharco, G., Gadre, S. Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A. S., Namkoong, H., Farhadi, A., Carmon, Y., Kornblith, S., et al · 2022
Cited alongside, same era.
Zhai, X., Kolesnikov, A., Houlsby, N., and Beyer, L · 2022
Cited alongside, same era.
Scaling laws for generative mixed-modal language models
Aghajanyan, A., Yu, L., Conneau, A., Hsu, W.-N., Hambardzumyan, K., Zhang, S., Roller, S., Goyal, N., Levy, O., and Zettlemoyer, L · 2023
Cited alongside, same era.
Why do we need weight decay in modern deep learning?
Andriushchenko, M., D’Angelo, F., Varre, A., and Flammarion, N · 2023
Cited alongside, same era.
Bi, X., Chen, D., Chen, G., Chen, S., Dai, D., Deng, C., Ding, H., Dong, K., Du, Q., Fu, Z., et al · 2024
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Does your data spark joy? performance gains from domain upsampling at the end of training
Blakeney, C., Paul, M., Larsen, B. W., Owen, S., and Frankle, J · 2024
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Griffin: Mixing gated linear recurrences with local attention for efficient language models
De, S., Smith, S. L., Fernando, A., Botev, A., Cristian-Muraru, G., Gu, A., Haroun, R., Berrada, L., Chen, Y., Srinivasan, S., Desjardins, G., Doucet, A., Budden, D., Teh, Y. W., Pascanu, R., Freitas, N. D., and Gulcehre, C · 2024
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Defazio, A., Yang, X., Mehta, H., Mishchenko, K., Khaled, A., and Cutkosky, A · 2024
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Understanding emergent abilities of language models from the loss perspective
Du, Z., Zeng, A., Dong, Y., and Tang, J · 2024
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Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al · 2024
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Language models scale reliably with over-training and on downstream tasks
Gadre, S. Y., Smyrnis, G., Shankar, V., Gururangan, S., Wortsman, M., Shao, R., Mercat, J., Fang, A., Li, J., Keh, S., Xin, R., Nezhurina, M., Vasiljevic, I., Jitsev, J., Dimakis, A. G., Ilharco, G., Song, S., Kollar, T., Carmon, Y., Dave, A., Heckel, R., Muennighoff, N., and Schmidt, L · 2024
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Scaling Laws for Data Filtering–Data Curation cannot be Compute Agnostic
Goyal, S., Maini, P., Lipton, Z. C., Raghunathan, A., and Kolter, J. Z · 2024
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Minicpm: Unveiling the potential of small language models with scalable training strategies
Hu, S., Tu, Y., Han, X., He, C., Cui, G., Long, X., Zheng, Z., Fang, Y., Huang, Y., Zhao, W., Zhang, X., Thai, Z. L., Zhang, K., Wang, C., Yao, Y., Zhao, C., Zhou, J., Cai, J., Zhai, Z., Ding, N., Jia, C., Zeng, G., Li, D., Liu, Z., and Sun, M · 2024
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Simple and scalable strategies to continually pre-train large language models
Ibrahim, A., Thérien, B., Gupta, K., Richter, M. L., Anthony, Q., Lesort, T., Belilovsky, E., and Rish, I · 2024
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Exponential moving average of weights in deep learning: Dynamics and benefits
Morales-Brotons, D., Vogels, T., and Hendrikx, H · 2024
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Reka core, flash, and edge: A series of powerful multimodal language models
Ormazabal, A., Zheng, C., d’Autume, C. d. M., Yogatama, D., Fu, D., Ong, D., Chen, E., Lamprecht, E., Pham, H., Ong, I., et al · 2024
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gzip predicts data-dependent scaling laws
Pandey, R · 2024
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Reconciling kaplan and chinchilla scaling laws
Pearce, T. and Song, J · 2024
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The fineweb datasets: Decanting the web for the finest text data at scale, 2024
Penedo, G., Kydlíček, H., allal, L. B., Lozhkov, A., Mitchell, M., Raffel, C., Werra, L. V., and Wolf, T · 2024
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Resolving discrepancies in compute-optimal scaling of language models
Porian, T., Wortsman, M., Jitsev, J., Schmidt, L., and Carmon, Y · 2024
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Jetmoe: Reaching llama2 performance with 0.1m dollars
Shen, Y., Guo, Z., Cai, T., and Qin, Z · 2024
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Roformer: Enhanced transformer with rotary position embedding
Su, J., Ahmed, M., Lu, Y., Pan, S., Bo, W., and Liu, Y · 2024
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Small-scale proxies for large-scale transformer training instabilities
Wortsman, M., Liu, P. J., Xiao, L., Everett, K. E., Alemi, A. A., Adlam, B., Co-Reyes, J. D., Gur, I., Kumar, A., Novak, R., Pennington, J., Sohl-Dickstein, J., Xu, K., Lee, J., Gilmer, J., and Kornblith, S · 2024
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