skip to main content
10.1145/3531146.3533229acmotherconferencesArticle/Chapter ViewAbstractPublication PagesfacctConference Proceedingsconference-collections
research-article
Open access

Predictability and Surprise in Large Generative Models

Published: 20 June 2022 Publication History

Abstract

Large-scale pre-training has recently emerged as a technique for creating capable, general-purpose, generative models such as GPT-3, Megatron-Turing NLG, Gopher, and many others. In this paper, we highlight a counterintuitive property of such models and discuss the policy implications of this property. Namely, these generative models have a paradoxical combination of predictable loss on a broad training distribution (as embodied in their ”scaling laws”), and unpredictable specific capabilities, inputs, and outputs. We believe that the high-level predictability and appearance of useful capabilities drives rapid development of such models, while the unpredictable qualities make it difficult to anticipate the consequences of model deployment. We go through examples of how this combination can lead to socially harmful behavior with examples from the literature and real world observations, and we also perform two novel experiments to illustrate our point about harms from unpredictability. Furthermore, we analyze how these conflicting properties combine to give model developers various motivations for deploying these models, and challenges that can hinder deployment. We conclude with a list of possible interventions the AI community may take to increase the chance of these models having a beneficial impact. We intend for this paper to be useful to policymakers who want to understand and regulate AI systems, technologists who care about the potential policy impact of their work, funders who want to support work addressing these challenges, and academics who want to analyze, critique, and potentially develop large generative models.

References

[1]
Neel Alex, Eli Lifland, Lewis Tunstall, Abhishek Thakur, Pegah Maham, C. Jess Riedel, Emmie Hine, Carolyn Ashurst, Paul Sedille, Alexis Carlier, Michael Noetel, and Andreas Stuhlmüller. 2021. RAFT: A Real-World Few-Shot Text Classification Benchmark. arXiv:2109.14076 [cs] (Nov. 2021). http://arxiv.org/abs/2109.14076 arXiv:2109.14076.
[2]
Dario Amodei, Danny Hernandez, Girish Sastry, Jack Clark, Greg Brockman, and Ilya Sutskever. 2018. AI and Compute. https://openai.com/blog/ai-and-compute/
[3]
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
[4]
Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Jackson Kernion, Kamal Ndousse, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, and Jared Kaplan. 2021. A General Language Assistant as a Laboratory for Alignment. arXiv:2112.00861 [cs] (Dec. 2021). http://arxiv.org/abs/2112.00861 arXiv:2112.00861.
[5]
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, and Charles Sutton. 2021. Program Synthesis with Large Language Models. arXiv:2108.07732 [cs] (Aug. 2021). http://arxiv.org/abs/2108.07732 arXiv:2108.07732.
[6]
Shahar Avin, Haydn Belfield, Miles Brundage, Gretchen Krueger, Jasmine Wang, Adrian Weller, Markus Anderljung, Igor Krawczuk, David Krueger, Jonathan Lebensold, Tegan Maharaj, and Noa Zilberman. 2021. Filling gaps in trustworthy development of AI. Science (Dec. 2021). https://doi.org/10.1126/science.abi7176 Publisher: American Association for the Advancement of Science.
[7]
Michelle Bao, Angela Zhou, Samantha Zottola, Brian Brubach, Sarah Desmarais, Aaron Horowitz, Kristian Lum, and Suresh Venkatasubramanian. 2021. It’s COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks. arXiv:2106.05498 [cs] (June 2021). http://arxiv.org/abs/2106.05498 arXiv:2106.05498.
[8]
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency(FAccT ’21). Association for Computing Machinery, New York, NY, USA, 610–623. https://doi.org/10.1145/3442188.3445922
[9]
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020. Language (Technology) is Power: A Critical Survey of ”Bias” in NLP. arXiv:2005.14050 [cs] (May 2020). http://arxiv.org/abs/2005.14050 arXiv:2005.14050.
[10]
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, Aditi Raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, and Percy Liang. 2021. On the Opportunities and Risks of Foundation Models. arXiv:2108.07258 [cs] (Aug. 2021). http://arxiv.org/abs/2108.07258 arXiv:2108.07258.
[11]
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack W. Rae, Erich Elsen, and Laurent Sifre. 2021. Improving language models by retrieving from trillions of tokens. arXiv:2112.04426 [cs] (Dec. 2021). http://arxiv.org/abs/2112.04426 arXiv:2112.04426.
[12]
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.). Vol. 33. Curran Associates, Inc., 1877–1901. https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
[13]
Miles Brundage, Shahar Avin, Jasmine Wang, Haydn Belfield, Gretchen Krueger, Gillian Hadfield, Heidy Khlaaf, Jingying Yang, Helen Toner, Ruth Fong, Tegan Maharaj, Pang Wei Koh, Sara Hooker, Jade Leung, Andrew Trask, Emma Bluemke, Jonathan Lebensold, Cullen O’Keefe, Mark Koren, Théo Ryffel, J. B. Rubinovitz, Tamay Besiroglu, Federica Carugati, Jack Clark, Peter Eckersley, Sarah de Haas, Maritza Johnson, Ben Laurie, Alex Ingerman, Igor Krawczuk, Amanda Askell, Rosario Cammarota, Andrew Lohn, David Krueger, Charlotte Stix, Peter Henderson, Logan Graham, Carina Prunkl, Bianca Martin, Elizabeth Seger, Noa Zilberman, Seán O hÉigeartaigh, Frens Kroeger, Girish Sastry, Rebecca Kagan, Adrian Weller, Brian Tse, Elizabeth Barnes, Allan Dafoe, Paul Scharre, Ariel Herbert-Voss, Martijn Rasser, Shagun Sodhani, Carrick Flynn, Thomas Krendl Gilbert, Lisa Dyer, Saif Khan, Yoshua Bengio, and Markus Anderljung. 2020. Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. arXiv:2004.07213 [cs] (April 2020). http://arxiv.org/abs/2004.07213 arXiv:2004.07213.
[14]
Ben Buchanan, Andrew Lohn, Micha Musser, and Katerina Sedova. 2021. Truth, Lies, and Automation. https://cset.georgetown.edu/publication/truth-lies-and-automation/
[15]
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, Alina Oprea, and Colin Raffel. 2021. Extracting Training Data from Large Language Models. arXiv:2012.07805 [cs] (June 2021). http://arxiv.org/abs/2012.07805 arXiv:2012.07805.
[16]
Edwin Cartlidge. 2019. Square Kilometre Array hit with further cost hike and delay. Physics World (Aug. 2019). https://physicsworld.com/a/square-kilometre-array-hit-with-further-cost-hike-and-delay/
[17]
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021. Evaluating Large Language Models Trained on Code. arXiv:2107.03374 [cs] (July 2021). http://arxiv.org/abs/2107.03374 arXiv:2107.03374.
[18]
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning. 2019. What Does BERT Look At? An Analysis of BERT’s Attention. arXiv:1906.04341 [cs] (June 2019). http://arxiv.org/abs/1906.04341 arXiv:1906.04341.
[19]
Kaleigh Clary, Emma Tosch, John Foley, and David Jensen. 2019. Let’s Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments. arXiv:1904.06312 [cs, stat] (April 2019). http://arxiv.org/abs/1904.06312 arXiv:1904.06312.
[20]
Kate Crawford. 2021. Atlas of AI. Yale University Press. https://yalebooks.yale.edu/book/9780300209570/atlas-ai
[21]
Emily Denton, Alex Hanna, Razvan Amironesei, Andrew Smart, Hilary Nicole, and Morgan Klaus Scheuerman. 2020. Bringing the People Back In: Contesting Benchmark Machine Learning Datasets. arXiv:2007.07399 [cs] (July 2020). http://arxiv.org/abs/2007.07399 arXiv:2007.07399.
[22]
Emily Dinan, Gavin Abercrombie, A. Stevie Bergman, Shannon Spruit, Dirk Hovy, Y.-Lan Boureau, and Verena Rieser. 2021. Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling. arXiv:2107.03451 [cs] (July 2021). http://arxiv.org/abs/2107.03451 arXiv:2107.03451.
[23]
Julia Dressel and Hany Farid. 2018. The accuracy, fairness, and limits of predicting recidivism. Science Advances (Jan. 2018). https://doi.org/10.1126/sciadv.aao5580 Publisher: American Association for the Advancement of Science.
[24]
Jasha Droppo and Oguz Elibol. 2021. Scaling Laws for Acoustic Models. arXiv:2106.09488 [cs, eess] (June 2021). http://arxiv.org/abs/2106.09488 arXiv:2106.09488.
[25]
Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathy Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V. Le, Yonghui Wu, Zhifeng Chen, and Claire Cui. 2021. GLaM: Efficient Scaling of Language Models with Mixture-of-Experts. arXiv:2112.06905 [cs] (Dec. 2021). http://arxiv.org/abs/2112.06905 arXiv:2112.06905.
[26]
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah. 2021. A Mathematical Framework for Transformer Circuits.
[27]
Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. 2016. On the (im)possibility of fairness. arXiv:1609.07236 [cs, stat] (Sept. 2016). http://arxiv.org/abs/1609.07236 arXiv:1609.07236.
[28]
Leo Gao, Jonathan Tow, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Kyle McDonell, Niklas Muennighoff, Jason Phang, Laria Reynolds, Eric Tang, Anish Thite, Ben Wang, Kevin Wang, and Andy Zou. 2021. A framework for few-shot language model evaluation. https://doi.org/10.5281/zenodo.5371628
[29]
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2021. Datasheets for Datasets. arXiv:1803.09010 [cs] (Dec. 2021). http://arxiv.org/abs/1803.09010 arXiv:1803.09010.
[30]
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith. 2020. RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models. ArXiv abs/2009.11462(2020).
[31]
Mary Gray and Siddharth Suri. 2019. Ghost Work. Mariner Books. https://ghostwork.info/
[32]
Soyeon Caren Han, Taejun Lim, Siqu Long, Bernd Burgstaller, and Josiah Poon. 2021. GLocal-K: Global and Local Kernels for Recommender Systems. (Aug. 2021). https://doi.org/10.1145/3459637.3482112
[33]
Laura Hanu and Unitary team. 2020. Detoxify. Published: Github. https://github.com/unitaryai/detoxify.
[34]
F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems 5, 4 (Dec. 2015), 19:1–19:19. https://doi.org/10.1145/2827872
[35]
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021. Measuring Massive Multitask Language Understanding. arXiv:2009.03300 [cs] (Jan. 2021). http://arxiv.org/abs/2009.03300 arXiv:2009.03300.
[36]
Dan Hendrycks, Nicholas Carlini, John Schulman, and Jacob Steinhardt. 2021. Unsolved Problems in ML Safety. arXiv:2109.13916 [cs] (Dec. 2021). http://arxiv.org/abs/2109.13916 arXiv:2109.13916.
[37]
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song. 2021. Natural Adversarial Examples. arXiv:1907.07174 [cs, stat] (March 2021). http://arxiv.org/abs/1907.07174 arXiv:1907.07174.
[38]
Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B. Brown, Prafulla Dhariwal, Scott Gray, Chris Hallacy, Benjamin Mann, Alec Radford, Aditya Ramesh, Nick Ryder, Daniel M. Ziegler, John Schulman, Dario Amodei, and Sam McCandlish. 2020. Scaling Laws for Autoregressive Generative Modeling. arXiv:2010.14701 [cs] (Nov. 2020). http://arxiv.org/abs/2010.14701 arXiv:2010.14701.
[39]
Danny Hernandez, Jared Kaplan, Tom Henighan, and Sam McCandlish. 2021. Scaling Laws for Transfer. arXiv:2102.01293 [cs] (Feb. 2021). http://arxiv.org/abs/2102.01293 arXiv:2102.01293.
[40]
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou. 2017. Deep Learning Scaling is Predictable, Empirically. arXiv:1712.00409 [cs, stat] (Dec. 2017). http://arxiv.org/abs/1712.00409 arXiv:1712.00409.
[41]
Daniel Ho, Jennifer King, Russell Wald, and Christopher Wan. 2021. Building a National AI Research Resource. White Paper. Stanford University. https://hai.stanford.edu/white-paper-building-national-ai-research-resource
[42]
Erik Hoel. 2021. Big Tech is replacing human artists with AI. https://erikhoel.substack.com/p/big-tech-is-replacing-human-artists
[43]
AI Now Institute. 2021. Democratize AI? How the proposed National AI Research Resource falls short. https://medium.com/@AINowInstitute/democratize-ai-how-the-proposed-national-ai-research-resource-falls-short-96ae5f67ccfa
[44]
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020. Scaling Laws for Neural Language Models. arXiv:2001.08361 [cs, stat] (Jan. 2020). http://arxiv.org/abs/2001.08361 arXiv:2001.08361.
[45]
Zachary Kenton, Tom Everitt, Laura Weidinger, Iason Gabriel, Vladimir Mikulik, and Geoffrey Irving. 2021. Alignment of Language Agents. arXiv:2103.14659 [cs] (March 2021). http://arxiv.org/abs/2103.14659 arXiv:2103.14659.
[46]
Josh Kenway, Francois Camille, Sasha Costanza-Chock, Deborah Raji, Inioluwa, and Joy Buolamwini. 2022. Bug Bounties For Algorithmic Harms?Technical Report. Algorithmic Justice League. https://www.ajl.org/bugs
[47]
Boseop Kim, HyoungSeok Kim, Sang-Woo Lee, Gichang Lee, Donghyun Kwak, Dong Hyeon Jeon, Sunghyun Park, Sungju Kim, Seonhoon Kim, Dongpil Seo, Heungsub Lee, Minyoung Jeong, Sungjae Lee, Minsub Kim, Suk Hyun Ko, Seokhun Kim, Taeyong Park, Jinuk Kim, Soyoung Kang, Na-Hyeon Ryu, Kang Min Yoo, Minsuk Chang, Soobin Suh, Sookyo In, Jinseong Park, Kyungduk Kim, Hiun Kim, Jisu Jeong, Yong Goo Yeo, Donghoon Ham, Dongju Park, Min Young Lee, Jaewook Kang, Inho Kang, Jung-Woo Ha, Woomyoung Park, and Nako Sung. 2021. What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers. arXiv:2109.04650 [cs] (Nov. 2021). http://arxiv.org/abs/2109.04650 arXiv:2109.04650.
[48]
Alex Knapp. 2012. How Much Does It Cost To Find A Higgs Boson?Forbes (June 2012). https://www.forbes.com/sites/alexknapp/2012/07/05/how-much-does-it-cost-to-find-a-higgs-boson/ Section: Tech.
[49]
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger (Eds.). Vol. 25. Curran Associates, Inc.https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
[50]
Connor Leahy. 2022. Announcing GPT-NeoX-20B. https://blog.eleuther.ai/announcing-20b/
[51]
Chuan Li. 2020. OpenAI’s GPT-3 Language Model: A Technical Overview. https://lambdalabs.com/blog/demystifying-gpt-3/
[52]
Opher Lieber, Or Sharir, Barak Lenz, and Yoav Shoham. 2021. Jurassic-1: Technical Details And Evaluation. Technical Report. AI21 Labs.
[53]
Andrew Lohn and Micha Musser. 2022. AI and Compute: How Much Longer Can Computing Power Drive Artificial Intelligence Progress?Technical Report. Center for Security and Emerging Technology. https://doi.org/10.51593/2021CA009
[54]
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019. Model Cards for Model Reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency (Jan. 2019), 220–229. https://doi.org/10.1145/3287560.3287596 arXiv:1810.03993.
[55]
Shakir Mohamed, Marie-Therese Png, and William Isaac. 2020. Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence. Philosophy & Technology 33 (Dec. 2020). https://doi.org/10.1007/s13347-020-00405-8
[56]
Nick Walton [@nickwalton00]. 2020. I’ve noticed a number of people using AI Dungeon to test GPT-3’s abilities. While it’s a great way to see how GPT-3 can power an interesting application. It’s a poor test of GPT-3’s abilities in general. The first generation of any custom prompt is actually GPT-2.https://twitter.com/nickwalton00/status/1289946861478936577
[57]
David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, and Jeff Dean. 2021. Carbon Emissions and Large Neural Network Training. arXiv:2104.10350 [cs] (April 2021). http://arxiv.org/abs/2104.10350 arXiv:2104.10350.
[58]
Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving. 2022. Red Teaming Language Models with Language Models. arXiv:2202.03286 [cs] (Feb. 2022). http://arxiv.org/abs/2202.03286 arXiv:2202.03286.
[59]
Alethea Power, Yuri Burda, Harri Edwards, Igor Babuschkin, and Vedant Misra. 2022. Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets. arXiv:2201.02177 [cs] (Jan. 2022). http://arxiv.org/abs/2201.02177 arXiv:2201.02177.
[60]
Shrimai Prabhumoye, Rafal Kocielnik, Mohammad Shoeybi, Anima Anandkumar, and Bryan Catanzaro. 2021. Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases. arXiv:2112.07868 [cs] (Dec. 2021). http://arxiv.org/abs/2112.07868 arXiv:2112.07868.
[61]
Gabriele Prato, Simon Guiroy, Ethan Caballero, Irina Rish, and Sarath Chandar. 2021. Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers. arXiv:2110.06990 [cs] (Oct. 2021). http://arxiv.org/abs/2110.06990 arXiv:2110.06990.
[62]
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning Transferable Visual Models From Natural Language Supervision. arXiv:2103.00020 [cs] (Feb. 2021). http://arxiv.org/abs/2103.00020 arXiv:2103.00020.
[63]
Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, Antonia Creswell, Nat McAleese, Amy Wu, Erich Elsen, Siddhant Jayakumar, Elena Buchatskaya, David Budden, Esme Sutherland, Karen Simonyan, Michela Paganini, Laurent Sifre, Lena Martens, Xiang Lorraine Li, Adhiguna Kuncoro, Aida Nematzadeh, Elena Gribovskaya, Domenic Donato, Angeliki Lazaridou, Arthur Mensch, Jean-Baptiste Lespiau, Maria Tsimpoukelli, Nikolai Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Toby Pohlen, Zhitao Gong, Daniel Toyama, Cyprien de Masson d’Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew Johnson, Blake Hechtman, Laura Weidinger, Iason Gabriel, William Isaac, Ed Lockhart, Simon Osindero, Laura Rimell, Chris Dyer, Oriol Vinyals, Kareem Ayoub, Jeff Stanway, Lorrayne Bennett, Demis Hassabis, Koray Kavukcuoglu, and Geoffrey Irving. 2021. Scaling Language Models: Methods, Analysis & Insights from Training Gopher. arXiv:2112.11446 [cs] (Dec. 2021). http://arxiv.org/abs/2112.11446 arXiv:2112.11446.
[64]
Inioluwa Deborah Raji, Emily M. Bender, Amandalynne Paullada, Emily Denton, and Alex Hanna. 2021. AI and the Everything in the Whole Wide World Benchmark. arXiv:2111.15366 [cs] (Nov. 2021). http://arxiv.org/abs/2111.15366 arXiv:2111.15366.
[65]
Inioluwa Deborah Raji and Joy Buolamwini. 2019. Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society(AIES ’19). Association for Computing Machinery, New York, NY, USA, 429–435. https://doi.org/10.1145/3306618.3314244
[66]
Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes. 2020. Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency(FAT* ’20). Association for Computing Machinery, New York, NY, USA, 33–44. https://doi.org/10.1145/3351095.3372873
[67]
Jonathan S. Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, and Nir Shavit. 2019. A Constructive Prediction of the Generalization Error Across Scales. arXiv:1909.12673 [cs, stat] (Dec. 2019). http://arxiv.org/abs/1909.12673 arXiv:1909.12673.
[68]
Cynthia Rudin, Caroline Wang, and Beau Coker. 2020. The Age of Secrecy and Unfairness in Recidivism Prediction. Harvard Data Science Review 2, 1 (March 2020). https://doi.org/10.1162/99608f92.6ed64b30
[69]
Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M. Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, and Alexander M. Rush. 2021. Multitask Prompted Training Enables Zero-Shot Task Generalization. arXiv:2110.08207 [cs] (Dec. 2021). http://arxiv.org/abs/2110.08207 arXiv:2110.08207.
[70]
Roy Schwartz, Jesse Dodge, Noah A. Smith, and Oren Etzioni. 2020. Green AI. Commun. ACM 63, 12 (Nov. 2020), 54–63. https://doi.org/10.1145/3381831
[71]
Jaime Sevilla, Pablo Villalobos, Juan Felipe Cerón, Matthew Burtell, Lennart Heim, Amogh B Nanjajjar, Anson Ho, Tamay Besiroglu, Marius Hobbhahn, and Jean-Stanislas Denain. 2021. Parameter, Compute and Data Trends in Machine Learning. https://docs.google.com/spreadsheets/d/1AAIebjNsnJj_uKALHbXNfn3_YsT6sHXtCU0q7OIPuc4/
[72]
Tom Simonite. 2021. What Really Happened When Google Ousted Timnit Gebru. Wired (June 2021). https://www.wired.com/story/google-timnit-gebru-ai-what-really-happened/ Section: tags.
[73]
Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, Elton Zhang, Rewon Child, Reza Yazdani Aminabadi, Julie Bernauer, Xia Song, Mohammad Shoeybi, Yuxiong He, Michael Houston, Saurabh Tiwary, and Bryan Catanzaro. 2022. Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model. arXiv:2201.11990 [cs] (Feb. 2022). http://arxiv.org/abs/2201.11990 arXiv:2201.11990.
[74]
Irene Solaiman and Christy Dennison. 2021. Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets. arXiv:2106.10328 [cs] (Nov. 2021). http://arxiv.org/abs/2106.10328 arXiv:2106.10328.
[75]
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019. Energy and Policy Considerations for Deep Learning in NLP. arXiv:1906.02243 [cs] (June 2019). http://arxiv.org/abs/1906.02243 arXiv:1906.02243.
[76]
Alex Tamkin, Miles Brundage, Jack Clark, and Deep Ganguli. 2021. Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models. arXiv:2102.02503 [cs] (Feb. 2021). http://arxiv.org/abs/2102.02503 arXiv:2102.02503.
[77]
Latitude Team. 2020. AI Dungeon: Dragon Model Upgrade. https://aidungeon.medium.com/ai-dungeon-dragon-model-upgrade-7e8ea579abfe
[78]
Will Thomas. 2020. Flagship Neutrino Project Working to Keep Costs Within Cap. https://www.aip.org/fyi/2020/flagship-neutrino-project-working-keep-costs-within-cap Publisher: American Institute of Physics.
[79]
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Kathleen Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Croak, Ed Chi, and Quoc Le. 2022. LaMDA: Language Models for Dialog Applications. arXiv:2201.08239 [cs] (Jan. 2022). http://arxiv.org/abs/2201.08239 arXiv:2201.08239.
[80]
Ted Underwood. 2021. Science fiction hasn’t prepared us to imagine machine learning.https://tedunderwood.com/2021/02/02/why-sf-hasnt-prepared-us-to-imagine-machine-learning/
[81]
Ben Wang and Aran Komatsuzaki. 2021. GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model. https://github.com/kingoflolz/mesh-transformer-jax
[82]
Shuohuan Wang, Yu Sun, Yang Xiang, Zhihua Wu, Siyu Ding, Weibao Gong, Shikun Feng, Junyuan Shang, Yanbin Zhao, Chao Pang, Jiaxiang Liu, Xuyi Chen, Yuxiang Lu, Weixin Liu, Xi Wang, Yangfan Bai, Qiuliang Chen, Li Zhao, Shiyong Li, Peng Sun, Dianhai Yu, Yanjun Ma, Hao Tian, Hua Wu, Tian Wu, Wei Zeng, Ge Li, Wen Gao, and Haifeng Wang. 2021. ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation. arXiv:2112.12731 [cs] (Dec. 2021). http://arxiv.org/abs/2112.12731 arXiv:2112.12731.
[83]
Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. 2021. Finetuned Language Models Are Zero-Shot Learners. arXiv:2109.01652 [cs] (Dec. 2021). http://arxiv.org/abs/2109.01652 arXiv:2109.01652.
[84]
Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, Zac Kenton, Sasha Brown, Will Hawkins, Tom Stepleton, Courtney Biles, Abeba Birhane, Julia Haas, Laura Rimell, Lisa Anne Hendricks, William Isaac, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2021. Ethical and social risks of harm from Language Models. arXiv:2112.04359 [cs] (Dec. 2021). http://arxiv.org/abs/2112.04359 arXiv:2112.04359.
[85]
Johannes Welbl, Amelia Glaese, Jonathan Uesato, Sumanth Dathathri, John Mellor, Lisa Anne Hendricks, Kirsty Anderson, Pushmeet Kohli, Ben Coppin, and Po-Sen Huang. 2021. Challenges in Detoxifying Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2021. Association for Computational Linguistics, Punta Cana, Dominican Republic, 2447–2469. https://doi.org/10.18653/v1/2021.findings-emnlp.210
[86]
Jess Whittlestone and Jack Clark. 2021. Why and How Governments Should Monitor AI Development. arXiv:2108.12427 [cs] (Aug. 2021). http://arxiv.org/abs/2108.12427 arXiv:2108.12427.
[87]
M. J. Wolf, K. W. Miller, and F. S. Grodzinsky. 2017. Why We Should Have Seen That Coming: Comments on Microsoft’s Tay “Experiment,” and Wider Implications. The ORBIT Journal 1, 2 (Jan. 2017), 1–12. https://doi.org/10.29297/orbit.v1i2.49
[88]
Shaohua Wu, Xudong Zhao, Tong Yu, Rongguo Zhang, Chong Shen, Hongli Liu, Feng Li, Hong Zhu, Jiangang Luo, Liang Xu, and Xuanwei Zhang. 2021. Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning. arXiv:2110.04725 [cs] (Oct. 2021). http://arxiv.org/abs/2110.04725 arXiv:2110.04725.
[89]
Jing Xu, Da Ju, Margaret Li, Y.-Lan Boureau, Jason Weston, and Emily Dinan. 2021. Bot-Adversarial Dialogue for Safe Conversational Agents. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tür, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou (Eds.). Association for Computational Linguistics, 2950–2968. https://doi.org/10.18653/v1/2021.naacl-main.235
[90]
Wei Zeng, Xiaozhe Ren, Teng Su, Hui Wang, Yi Liao, Zhiwei Wang, Xin Jiang, ZhenZhang Yang, Kaisheng Wang, Xiaoda Zhang, Chen Li, Ziyan Gong, Yifan Yao, Xinjing Huang, Jun Wang, Jianfeng Yu, Qi Guo, Yue Yu, Yan Zhang, Jin Wang, Hengtao Tao, Dasen Yan, Zexuan Yi, Fang Peng, Fangqing Jiang, Han Zhang, Lingfeng Deng, Yehong Zhang, Zhe Lin, Chao Zhang, Shaojie Zhang, Mingyue Guo, Shanzhi Gu, Gaojun Fan, Yaowei Wang, Xuefeng Jin, Qun Liu, and Yonghong Tian. 2021. PanGu-$\alpha$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation. arXiv:2104.12369 [cs] (April 2021). http://arxiv.org/abs/2104.12369 arXiv:2104.12369.
[91]
Donglin Zhuang, Xingyao Zhang, Shuaiwen Leon Song, and Sara Hooker. 2021. Randomness In Neural Network Training: Characterizing The Impact of Tooling. arXiv:2106.11872 [cs] (June 2021). http://arxiv.org/abs/2106.11872 arXiv:2106.11872.

Cited By

View all
  • (2025)A survey of emerging applications of large language models for problems in mechanics, product design, and manufacturingAdvanced Engineering Informatics10.1016/j.aei.2024.10306664(103066)Online publication date: Mar-2025
  • (2025)Large Language Model Evaluation Criteria Framework in Healthcare: Fuzzy MCDM ApproachSN Computer Science10.1007/s42979-024-03533-66:1Online publication date: 2-Jan-2025
  • (2024)A language model's guide through latent spaceProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694100(49655-49687)Online publication date: 21-Jul-2024
  • Show More Cited By

Index Terms

  1. Predictability and Surprise in Large Generative Models
    Index terms have been assigned to the content through auto-classification.

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency
    June 2022
    2351 pages
    ISBN:9781450393522
    DOI:10.1145/3531146
    This work is licensed under a Creative Commons Attribution International 4.0 License.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 20 June 2022

    Check for updates

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    FAccT '22
    Sponsor:

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)5,045
    • Downloads (Last 6 weeks)644
    Reflects downloads up to 06 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2025)A survey of emerging applications of large language models for problems in mechanics, product design, and manufacturingAdvanced Engineering Informatics10.1016/j.aei.2024.10306664(103066)Online publication date: Mar-2025
    • (2025)Large Language Model Evaluation Criteria Framework in Healthcare: Fuzzy MCDM ApproachSN Computer Science10.1007/s42979-024-03533-66:1Online publication date: 2-Jan-2025
    • (2024)A language model's guide through latent spaceProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694100(49655-49687)Online publication date: 21-Jul-2024
    • (2024)Generalization to new sequential decision making tasks with in-context learningProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693784(42138-42158)Online publication date: 21-Jul-2024
    • (2024)Compositional capabilities of autoregressive transformersProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693781(42074-42103)Online publication date: 21-Jul-2024
    • (2024)PositionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693509(35340-35353)Online publication date: 21-Jul-2024
    • (2024)Position: TRUSTLLMProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692883(20166-20270)Online publication date: 21-Jul-2024
    • (2024)LEBERT :Lite and Efficiently Optimized BERT PRetraining ApproachInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-15219(110-114)Online publication date: 23-Jan-2024
    • (2024)Privacy Preserving Data Analysis With Generative AIAI Techniques for Securing Medical and Business Practices10.4018/979-8-3693-8939-3.ch014(391-410)Online publication date: 13-Sep-2024
    • (2024)Emotion-Aware Scene Adaptation: A Bandwidth-Efficient Approach for Generating Animated ShortsSensors10.3390/s2405166024:5(1660)Online publication date: 4-Mar-2024
    • Show More Cited By

    View Options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Login options

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media