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How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce \textit{Pythia}, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters.
Megatron-LM: Training multi-billion parameter language models using model parallelism
Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., and Catanzaro, B · 1909
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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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A neural scaling law from the dimension of the data manifold
Sharma, U. and Kaplan, J · 2004
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Scaling laws for autoregressive generative modeling
Henighan, T., Kaplan, J., Katz, M., Chen, M., Hesse, C., Jackson, J., Jun, H., Brown, T. B., Dhariwal, P., Gray, S., Hallacy, C., Mann, B., Radford, A., Ramesh, A., Ryder, N., Ziegler, D. M., Schulman, J., Amodei, D., and McCandlish, S · 2010
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The lambada dataset: Word prediction requiring a broad discourse context
Paperno, D., Kruszewski, G., Lazaridou, A., Pham, Q. N., Bernardi, R., Pezzelle, S., Baroni, M., Boleda, G., and Fernández, R · 2016
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Accurate, large minibatch SGD: training imagenet in 1 hour
Goyal, P., Dollár, P., Girshick, R. B., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K · 2017
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Joshi, M., Choi, E., Weld, D. S., and Zettlemoyer, L · 2017
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., and Chang, K.-W · 2017
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An empirical model of large-batch training
McCandlish, S., Kaplan, J., Amodei, D., and Team, O. D · 2018
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Gender bias in coreference resolution: Evaluation and debiasing methods
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., and Chang, K.-W · 2018
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Identifying and reducing gender bias in word-level language models
Bordia, S. and Bowman, S · 2019
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Understanding the origins of bias in word embeddings
Brunet, M.-E., Alkalay-Houlihan, C., Anderson, A., and Zemel, R · 2019
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The secret sharer: Evaluating and testing unintended memorization in neural networks
Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., and Song, D · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
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Asynchronous pipeline for processing huge corpora on medium to low resource infrastructures
Suárez, P. J. O., Sagot, B., and Romary, L · 2019
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Huggingface’s transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al · 2019
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Which algorithmic choices matter at which batch sizes? insights from a noisy quadratic model
Zhang, G., Li, L., Nado, Z., Martens, J., Sachdeva, S., Dahl, G., Shallue, C., and Grosse, R. B · 2019
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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., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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Language id in the wild: Unexpected challenges on the path to a thousand-language web text corpus
Caswell, I., Breiner, T., van Esch, D., and Bapna, A · 2020
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Unsupervised cross-lingual representation learning at scale
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, Guillaume andGuzmán, F., Grave, E., Ott, Myle andZettlemoyer, L., and Stoyanov, V · 2020
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Underspecification presents challenges for credibility in modern machine learning
D’Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., 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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CrowS-pairs: A challenge dataset for measuring social biases in masked language models
Nangia, N., Vania, C., Bhalerao, R., and Bowman, S. R · 2020
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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 · 2020
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ZeRO: Memory optimizations toward training trillion parameter models
Rajbhandari, S., Rasley, J., Ruwase, O., and He, Y · 2020
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GPT-NeoX: Large scale autoregressive language modeling in PyTorch, 8 2021
Andonian, A., Anthony, Q., Biderman, S., Black, S., Gali, P., Gao, L., Hallahan, E., Levy-Kramer, J., Leahy, C., Nestler, L., Parker, K., Pieler, M., Purohit, S., Songz, T., Phil, W., and Weinbach, S · 2021
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The values encoded in machine learning research
Birhane, A., Kalluri, P., Card, D., Agnew, W., Dotan, R., and Bao, M · 2021
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GPT-Neo: Large scale autoregressive language modeling with Mesh-TensorFlow
Black, S., Gao, L., Wang, P., Leahy, C., and Biderman, S · 2021
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Stereotyping Norwegian salmon: An inventory of pitfalls in fairness benchmark datasets
Blodgett, S. L., Lopez, G., Olteanu, A., Sim, R., and Wallach, H · 2021
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Extracting training data from large language models
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al · 2021
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Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Such, F. P., Cummings, D., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W. H., Nichol, A., Paino, A., Tezak, N., Tang, J., Babuschkin, I., Balaji, S., Jain, S., Saunders, W., Hesse, C., Carr, A. N., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., and Zaremba, W · 2021
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Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you?
Choenni, R., Shutova, E., and van Rooij, R · 2021
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Documenting large webtext corpora: A case study on the colossal clean crawled corpus
Dodge, J., Sap, M., Marasović, A., Agnew, W., Ilharco, G., Groeneveld, D., Mitchell, M., and Gardner, M · 2021
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On the sizes of openai api models
Gao, L · 2021
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A framework for few-shot language model evaluation
Gao, L., Tow, J., Biderman, S., Black, S., DiPofi, A., Foster, C., Golding, L., Hsu, J., McDonell, K., Muennighoff, N., Phang, J., Reynolds, L., Tang, E., Thite, A., Wang, B., Wang, K., and Zou, A · 2021
Cited alongside, same era.
Scaling laws for neural machine translation
Ghorbani, B., Firat, O., Freitag, M., Bapna, A., Krikun, M., Garcia, X., Chelba, C., and Cherry, C · 2021
Cited alongside, same era.
Hernandez, D., Kaplan, J., Henighan, T., and McCandlish, S · 2021
Cited alongside, same era.
A systematic study of bias amplification
Hall, M., van der Maaten, L., Gustafson, L., and Adcock, A · 2022
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Scaling laws and interpretability of learning from repeated data
Hernandez, D., Brown, T., Conerly, T., DasSarma, N., Drain, D., El-Showk, S., Elhage, N., Hatfield-Dodds, Z., Henighan, T., Hume, T., Johnston, S., Mann, B., Olah, C., Olsson, C., Amodei, D., Joseph, N., Kaplan, J., and McCandlish, S · 2022
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Quantifying societal bias amplification in image captioning
Hirota, Y., Nakashima, Y., and Garcia, N · 2022
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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
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Membership inference attacks on machine learning: A survey
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Highly accurate protein structure prediction with alphafold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al · 2021
Cited alongside, same era.
Mistral - a journey towards reproducible language model training, 2021
Karamcheti, S., Orr, L., Bolton, J., Zhang, T., Goel, K., Narayan, A., Bommasani, R., Narayanan, D., Hashimoto, T., Jurafsky, D., Manning, C. D., Potts, C., Ré, C., and Liang, P · 2021
Cited alongside, same era.
Quality at a glance: An audit of web-crawled multilingual datasets
Kreutzer, J., Caswell, I., Wang, L., Wahab, A., van Esch, D., Ulzii-Orshikh, N., Tapo, A., Subramani, N., Sokolov, A., Sikasote, C., Setyawan, M., Sarin, S., Samb, S., Sagot, B., Rivera, C., Rios, A., Papadimitriou, I., Osei, S., Suárez, P. O., Orife, I., Ogueji, K., Rubungo, A. N., Nguyen, T. Q., Müller, M., Müller, A., Muhammad, S. H., Muhammad, N., Mnyakeni, A., Mirzakhalov, J., Matangira, T., Leong, C., Lawson, N., Kudugunta, S., Jernite, Y., Jenny, M., Firat, O., Dossou, B. F. P., Dlamini, S., de Silva, N., Çabuk Ballı, S., Biderman, S., Battisti, A., Baruwa, A., Bapna, A., Baljekar, P., Azime, I. A., Awokoya, A., Ataman, D., Ahia, O., Ahia, O., Agrawal, S., and Adeyemi, M · 2021
Cited alongside, same era.
Deduplicating training data makes language models better
Lee, K., Ippolito, D., Nystrom, A., Zhang, C., Eck, D., Callison-Burch, C., and Carlini, N · 2021
Cited alongside, same era.
Collecting a large-scale gender bias dataset for coreference resolution and machine translation
Levy, S., Lazar, K., and Stanovsky, G · 2021
Cited alongside, same era.
Jurassic-1: Technical details and evaluation
Lieber, O., Sharir, O., Lenz, B., and Shoham, Y · 2021
Cited alongside, same era.
Acquisition of chess knowledge in AlphaZero
McGrath, T., Kapishnikov, A., Tomašev, N., Pearce, A., Hassabis, D., Kim, B., Paquet, U., and Kramnik, V · 2021
Cited alongside, same era.
Hu, H., Salcic, Z., Sun, L., Dobbie, G., Yu, P. S., and Zhang, X · 2022
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Are large pre-trained language models leaking your personal information?
Huang, J., Shao, H., and Chang, K. C.-C · 2022
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Preventing verbatim memorization in language models gives a false sense of privacy
Ippolito, D., Tramèr, F., Nasr, M., Zhang, C., Jagielski, M., Lee, K., Choquette-Choo, C. A., and Carlini, N · 2022
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Measuring forgetting of memorized training examples
Jagielski, M., Thakkar, O., Tramer, F., Ippolito, D., Lee, K., Carlini, N., Wallace, E., Song, S., Thakurta, A., Papernot, N., et al · 2022
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Data governance in the age of large-scale data-driven language technology
Jernite, Y., Nguyen, H., Biderman, S., Rogers, A., Masoud, M., Danchev, V., Tan, S., Luccioni, A. S., Subramani, N., Johnson, I., et al · 2022
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Large language models struggle to learn long-tail knowledge
Kandpal, N., Deng, H., Roberts, A., Wallace, E., and Raffel, C · 2022
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Efficient gender debiasing of pre-trained indic language models
Kirtane, N., Manushree, V., and Kane, A · 2022
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The bigscience ROOTS corpus: A 1.6TB composite multilingual dataset
Laurençon, H., Saulnier, L., Wang, T., Akiki, C., del Moral, A. V., Scao, T. L., Werra, L. V., Mou, C., Ponferrada, E. G., Nguyen, H., Frohberg, J., Šaško, M., Lhoest, Q., McMillan-Major, A., Dupont, G., Biderman, S., Rogers, A., allal, L. B., Toni, F. D., Pistilli, G., Nguyen, O., Nikpoor, S., Masoud, M., Colombo, P., de la Rosa, J., Villegas, P., Thrush, T., Longpre, S., Nagel, S., Weber, L., Muñoz, M. R., Zhu, J., Strien, D. V., Alyafeai, Z., Almubarak, K., Chien, V. M., Gonzalez-Dios, I., Soroa, A., Lo, K., Dey, M., Suarez, P. O., Gokaslan, A., Bose, S., Adelani, D. I., Phan, L., Tran, H., Yu, I., Pai, S., Chim, J., Lepercq, V., Ilic, S., Mitchell, M., Luccioni, S., and Jernite, Y · 2022
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What language model to train if you have one million GPU hours?
Le Scao, T., Wang, T., Hesslow, D., Saulnier, L., Bekman, S., Bari, M. S., Biderman, S., Elsahar, H., Phang, J., Press, O., et al · 2022
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Mallen, A., Asai, A., Zhong, V., Das, R., Hajishirzi, H., and Khashabi, D · 2022
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McMillan-Major, A., Alyafeai, Z., Biderman, S., Chen, K., De Toni, F., Dupont, G., Elsahar, H., Emezue, C., Aji, A. F., Ilić, S., et al · 2022
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French CrowS-pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English
Névéol, A., Dupont, Y., Bezançon, J., and Fort, K · 2022
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Lifting the curse of multilinguality by pre-training modular transformers
Pfeiffer, J., Goyal, N., Lin, X. V., Li, X., Cross, J., Riedel, S., and Artetxe, M · 2022
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Eleutherai: Going beyond” open science” to” science in the open”
Phang, J., Bradley, H., Gao, L., Castricato, L., and Biderman, S · 2022
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Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
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Impact of pretraining term frequencies on few-shot reasoning
Razeghi, Y., Logan IV, R. L., Gardner, M., and Singh, S · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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BLOOM: A 176B-parameter open-access multilingual language model
Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A. S., Yvon, F., Gallé, M., Tow, J., Rush, A. M., Biderman, S., Webson, A., Ammanamanchi, P. S., Wang, T., Sagot, B., Muennighoff, N., del Moral, A. V., Ruwase, O., Bawden, R., Bekman, S., McMillan-Major, A., Beltagy, I., Nguyen, H., Saulnier, L., Tan, S., Suarez, P. O., Sanh, V., Laurençon, H., Jernite, Y., Launay, J., Mitchell, M., Raffel, C., Gokaslan, A., Simhi, A., Soroa, A., Aji, A. F., Alfassy, A., Rogers, A., Nitzav, A. K., Xu, C., Mou, C., Emezue, C., Klamm, C., Leong, C., van Strien, D., Adelani, D. I., Radev, D., Ponferrada, E. G., Levkovizh, E., Kim, E., Natan, E. B., De Toni, F., Dupont, G., Kruszewski, G., Pistilli, G., Elsahar, H., Benyamina, H., Tran, H., Yu, I., Abdulmumin, I., Johnson, I., Gonzalez-Dios, I., de la Rosa, J., Chim, J., Dodge, J., Zhu, J., Chang, J., Frohberg, J., Tobing, J., Bhattacharjee, J., Almubarak, K., Chen, K., Lo, K., Von Werra, L., Weber, L., Phan, L., allal, L. B., Tanguy, L., Dey, M., Muñoz, M. R., Masoud, M., Grandury, M., Šaško, M., Huang, M., Coavoux, M., Singh, M., Jiang, M. T.-J., Vu, M. C., Jauhar, M. A., Ghaleb, M., Subramani, N., Kassner, N., Khamis, N., Nguyen, O., Espejel, O., de Gibert, O., Villegas, P., Henderson, P., Colombo, P., Amuok, P., Lhoest, Q., Harliman, R., Bommasani, R., López, R. L., Ribeiro, R., Osei, S., Pyysalo, S., Nagel, S., Bose, S., Muhammad, S. H., Sharma, S., Longpre, S., Nikpoor, S., Silberberg, S., Pai, S., Zink, S., Torrent, T. T., Schick, T., Thrush, T., Danchev, V., Nikoulina, V., Laippala, V., Lepercq, V., Prabhu, V., Alyafeai, Z., Talat, Z., Raja, A., Heinzerling, B., Si, C., Taşar, D. E., Salesky, E., Mielke, S. J., Lee, W. Y., Sharma, A., Santilli, A., Chaffin, A., Stiegler, A., Datta, D., Szczechla, E., Chhablani, G., Wang, H., Pandey, H., Strobelt, H., Fries, J. A., Rozen, J., Gao, L., Sutawika, L., Bari, M. S., Al-shaibani, M. S., Manica, M., Nayak, N., Teehan, R., Albanie, S., Shen, S., Ben-David, S., Bach, S. H., Kim, T., Bers, T., Fevry, T., Neeraj, T., Thakker, U., Raunak, V., Tang, X., Yong, Z.-X., Sun, Z., Brody, S., Uri, Y., Tojarieh, H., Roberts, A., Chung, H. W., Tae, J., Phang, J., Press, O., Li, C., Narayanan, D., Bourfoune, H., Casper, J., Rasley, J., Ryabinin, M., Mishra, M., Zhang, M., Shoeybi, M., Peyrounette, M., Patry, N., Tazi, N., Sanseviero, O., von Platen, P., Cornette, P., Lavallée, P. F., Lacroix, R., Rajbhandari, S., Gandhi, S., Smith, S., Requena, S., Patil, S., Dettmers, T., Baruwa, A., Singh, A., Cheveleva, A., Ligozat, A.-L., Subramonian, A., Névéol, A., Lovering, C., Garrette, D., Tunuguntla, D., Reiter, E., Taktasheva, E., Voloshina, E., Bogdanov, E., Winata, G. I., Schoelkopf, H., Kalo, J.-C., Novikova, J., Forde, J. Z., Clive, J., Kasai, J., Kawamura, K., Hazan, L., Carpuat, M., Clinciu, M., Kim, N., Cheng, N., Serikov, O., Antverg, O., van der Wal, O., Zhang, R., Zhang, R., Gehrmann, S., Mirkin, S., Pais, S., Shavrina, T., Scialom, T., Yun, T., Limisiewicz, T., Rieser, V., Protasov, V., Mikhailov, V., Pruksachatkun, Y., Belinkov, Y., Bamberger, Z., Kasner, Z., Rueda, A., Pestana, A., Feizpour, A., Khan, A., Faranak, A., Santos, A., Hevia, A., Unldreaj, A., Aghagol, A., Abdollahi, A., Tammour, A., HajiHosseini, A., Behroozi, B., Ajibade, B., Saxena, B., Ferrandis, C. M., Contractor, D., Lansky, D., David, D., Kiela, D., Nguyen, D. A., Tan, E., Baylor, E., Ozoani, E., Mirza, F., Ononiwu, F., Rezanejad, H., Jones, H., Bhattacharya, I., Solaiman, I., Sedenko, I., Nejadgholi, I., Passmore, J., Seltzer, J., Sanz, J. B., Dutra, L., Samagaio, M., Elbadri, M., Mieskes, M., Gerchick, M., Akinlolu, M., McKenna, M., Qiu, M., Ghauri, M., Burynok, M., Abrar, N., Rajani, N., Elkott, N., Fahmy, N., Samuel, O., An, R., Kromann, R., Hao, R., Alizadeh, S., Shubber, S., Wang, S., Roy, S., Viguier, S., Le, T., Oyebade, T., Le, T., Yang, Y., Nguyen, Z., Kashyap, A. R., Palasciano, A., Callahan, A., Shukla, A., Miranda-Escalada, A., Singh, A., Beilharz, B., Wang, B., Brito, C., Zhou, C., Jain, C., Xu, C., Fourrier, C., Periñán, D. L., Molano, D., Yu, D., Manjavacas, E., Barth, F., Fuhrimann, F., Altay, G., Bayrak, G., Burns, G., Vrabec, H. U., Bello, I., Dash, I., Kang, J., Giorgi, J., Golde, J., Posada, J. D., Sivaraman, K. R., Bulchandani, L., Liu, L., Shinzato, L., de Bykhovetz, M. H., Takeuchi, M., Pàmies, M., Castillo, M. A., Nezhurina, M., Sänger, M., Samwald, M., Cullan, M., Weinberg, M., De Wolf, M., Mihaljcic, M., Liu, M., Freidank, M., Kang, M., Seelam, N., Dahlberg, N., Broad, N. M., Muellner, N., Fung, P., Haller, P., Chandrasekhar, R., Eisenberg, R., Martin, R., Canalli, R., Su, R., Su, R., Cahyawijaya, S., Garda, S., Deshmukh, S. S., Mishra, S., Kiblawi, S., Ott, S., Sang-aroonsiri, S., Kumar, S., Schweter, S., Bharati, S., Laud, T., Gigant, T., Kainuma, T., Kusa, W., Labrak, Y., Bajaj, Y. S., Venkatraman, Y., Xu, Y., Xu, Y., Xu, Y., Tan, Z., Xie, Z., Ye, Z., Bras, M., Belkada, Y., and Wolf, T · 2022
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On the effect of pretraining corpora on in-context learning by a large-scale language model
Shin, S., Lee, S.-W., Ahn, H., Kim, S., Kim, H., Kim, B., Cho, K., Lee, G., Park, W., Ha, J.-W., and Sung, N · 2022
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Cross-loss influence functions to explain deep network representations
Silva, A., Chopra, R., and Gombolay, M · 2022
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Smith, S., Patwary, M., Norick, B., LeGresley, P., Rajbhandari, S., Casper, J., Liu, Z., Prabhumoye, S., Zerveas, G., Korthikanti, V., Zhang, E., Child, R., Aminabadi, R. Y., Bernauer, J., Song, X., Shoeybi, M., He, Y., Houston, M., Tiwary, S., and Catanzaro, B · 2022
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Memorization without overfitting: Analyzing the training dynamics of large language models
Tirumala, K. N. B., Markosyan, A. H., Zettlemoyer, L., and Aghajanyan, A · 2022
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Training trajectories of language models across scales, 2022
Xia, M., Artetxe, M., Zhou, C., Lin, X. V., Pasunuru, R., Chen, D., Zettlemoyer, L., and Stoyanov, V · 2022
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A systematic evaluation of large language models of code
Xu, F. F., Alon, U., Neubig, G., and Hellendoorn, V. J · 2022
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Glm-130b: An open bilingual pre-trained model
Zeng, A., Liu, X., Du, Z., Wang, Z., Lai, H., Ding, M., Yang, Z., Xu, Y., Zheng, W., Xia, X., et al · 2022
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OPT: Open pre-trained transformer language models
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