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We investigate the potential implications of large language models (LLMs), such as Generative Pre-trained Transformers (GPTs), on the U.S.
Language models are few-shot learners
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The dynamo and the computer: an historical perspective on the modern productivity paradox
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General purpose technologies ‘engines of growth’?
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Technical progress and co-invention in computing and in the uses of computers
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Computerisation and wage dispersion: an analytical reinterpretation
Bresnahan, T. F. (1999) · 1999
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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. (2020) · 2001
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Technical change, inequality, and the labor market
Acemoglu, D. (2002) · 2002
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Information technology, workplace organization, and the demand for skilled labor: Firm-level evidence
Bresnahan, T. F., Brynjolfsson, E., and Hitt, L. M. (2002) · 2002
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The skill content of recent technological change: An empirical exploration
Autor, D. H., Levy, F., and Murnane, R. J. (2003) · 2003
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Economic transformations: general purpose technologies and long-term economic growth
Lipsey, R. G., Carlaw, K. I., and Bekar, C. T. (2005) · 2005
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The polarization of the us labor market
Autor, D. H., Katz, L. F., and Kearney, M. S. (2006) · 2006
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Generalized task markets for human and machine computation
Shahaf, D. and Horvitz, E. (2010) · 2010
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Wage inequality, technology and trade: 21st century evidence
Van Reenen, J. (2011) · 2011
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The cost disease: Why computers get cheaper and health care doesn’t
Baumol, W. J. (2012) · 2012
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Learning to hire teams
Singla, A. K., Horvitz, E., Kohli, P., and Krause, A. (2015) · 2015
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Tasks, automation, and the rise in us wage inequality
Acemoglu, D. and Restrepo, P. (2022b) · 2016
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Revisiting the risk of automation
Arntz, M., Gregory, T., and Zierahn, U. (2017) · 2017
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What can machine learning do? workforce implications
Brynjolfsson, E. and Mitchell, T. (2017) · 2017
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The future of employment: How susceptible are jobs to computerisation?
Frey, C. B. and Osborne, M. A. (2017) · 2017
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The race between man and machine: Implications of technology for growth, factor shares, and employment
Acemoglu, D. and Restrepo, P. (2018) · 2018
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Artificial intelligence and economic growth
Aghion, P., Jones, B. F., and Jones, C. I. (2018) · 2018
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New frontiers: The origins and content of new work, 1940–2018
Autor, D., Chin, C., Salomons, A. M., and Seegmiller, B. (2022a) · 2018
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Artificial intelligence and jobs: The role of demand
Bessen, J. (2018) · 2018
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What can machines learn, and what does it mean for occupations and the economy?
Brynjolfsson, E., Mitchell, T., and Rock, D. (2018) · 2018
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The impact of artificial intelligence on innovation: An exploratory analysis
Cockburn, I. M., Henderson, R., and Stern, S. (2018) · 2018
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A method to link advances in artificial intelligence to occupational abilities
Felten, E. W., Raj, M., and Seamans, R. (2018) · 2018
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When will ai exceed human performance? evidence from ai experts
Grace, K., Salvatier, J., Dafoe, A., Zhang, B., and Evans, O. (2018) · 2018
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Artificial intelligence in service
Huang, M.-H. and Rust, R. T. (2018) · 2018
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Artificial intelligence and its implications for income distribution and unemployment
Korinek, A. and Stiglitz, J. E. (2018) · 2018
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Automation and new tasks: How technology displaces and reinstates labor
Acemoglu, D. and Restrepo, P. (2019) · 2019
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Artificial intelligence technologies and aggregate growth prospects
Bresnahan, T. (2019) · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019) · 2019
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The technology trap
Frey, C. B. (2019) · 2019
Advanced technologies adoption and use by us firms: Evidence from the annual business survey
Zolas, N., Kroff, Z., Brynjolfsson, E., McElheran, K., Beede, D. N., Buffington, C., Goldschlag, N., Foster, L., and Dinlersoz, E. (2021) · 2021
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Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., Joseph, N., Kadavath, S., Kernion, J., Conerly, T., El-Showk, S., Elhage, N., Hatfield-Dodds, Z., Hernandez, D., Hume, T., Johnston, S., Kravec, S., Lovitt, L., Nanda, N., Olsson, C., Amodei, D., Brown, T., Clark, J., McCandlish, S., Olah, C., Mann, B., and Kaplan, J. (2022) · 2022
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Employment by detailed occupation
BLS (2022) · 2022
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LangChain
Chase, H. (2022) · 2022
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Innovae: Generative ai for understanding patents and innovation
Cheng, Z., Lee, D., and Tambe, P. (2022) · 2022
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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) · 2019
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Engineering value: The returns to technological talent and investments in artificial intelligence
Rock, D. (2019) · 2019
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Release strategies and the social impacts of language models
Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., Radford, A., Krueger, G., Kim, J. W., Kreps, S., McCain, M., Newhouse, A., Blazakis, J., McGuffie, K., and Wang, J. (2019) · 2019
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Ai and jobs: Evidence from online vacancies
Acemoglu, D., Autor, D., Hazell, J., and Restrepo, P. (2020) · 2020
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The impact of artificial intelligence on the labor market
Webb, M. (2020) · 2020
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Persistent anti-muslim bias in large language models
Abid, A., Farooqi, M., and Zou, J. (2021) · 2021
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A hazard analysis framework for code synthesis large language models
Khlaaf, H., Mishkin, P., Achiam, J., Krueger, G., and Brundage, M. (2022) · 2022
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New modes of learning enabled by ai chatbots: Three methods and assignments
Mollick, E. R. and Mollick, L. (2022) · 2022
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Introducing chatgpt
OpenAI (2022) · 2022
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · 2022
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Large pre-trained language models contain human-like biases of what is right and wrong to do
Schramowski, P., Turan, C., Andersen, N., Rothkopf, C. A., and Kersting, K. (2022) · 2022
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An information-theoretic approach to prompt engineering without ground truth labels
Sorensen, T., Robinson, J., Rytting, C., Shaw, A., Rogers, K., Delorey, A., Khalil, M., Fulda, N., and Wingate, D. (2022) · 2022
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Lamda: Language models for dialog applications
Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H.-T., Jin, A., Bos, T., Baker, L., Du, Y., et al. (2022) · 2022
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Taxonomy of risks posed by language models
Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., Biles, C., Brown, S., Kenton, Z., Hawkins, W., Stepleton, T., Birhane, A., Hendricks, L. A., Rimell, L., Isaac, W., Haas, J., Legassick, S., Irving, G., and Gabriel, I. (2022) · 2022
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Quantifying the Distribution of Machine Learning’s Impact on Work
Brynjolfsson, E., Frank, M. R., Mitchell, T., Rahwan, I., and Rock, D. (2023) · 2023
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Why ChatGPT Is the Fastest Growing Web Platform Ever | Time
Chow, A. R. (2023) · 2023
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Nearly a third of white collar workers have tried chatgpt or other ai programs, according to a new survey
Constantz, J. (2023) · 2023
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How will language modelers like chatgpt affect occupations and industries?
Felten, E., Raj, M., and Seamans, R. (2023) · 2023
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Could machine learning be a general purpose technology? a comparison of emerging technologies using data from online job postings
Goldfarb, A., Taska, B., and Teodoridis, F. (2023) · 2023
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Generative language models and automated influence operations: Emerging threats and potential mitigations
Goldstein, J. A., Sastry, G., Musser, M., DiResta, R., Gentzel, M., and Sedova, K. (2023) · 2023
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Large language models as simulated economic agents: What can we learn from homo silicus?
Horton, J. J. (2023) · 2023
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Language models and cognitive automation for economic research
Korinek, A. (2023) · 2023
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Augmented language models: a survey
Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al. (2023) · 2023
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Experimental evidence on the productivity effects of generative artificial intelligence
Noy, S. and Zhang, W. (2023) · 2023
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O*net 27.2 database
O*NET (2023) · 2023
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The impact of ai on developer productivity: Evidence from github copilot
Peng, S., Kalliamvakou, E., Cihon, P., and Demirer, M. (2023) · 2023
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1 in 4 companies have already replaced workers with chatgpt
ResumeBuilder.com (2023) · 2023
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Toolformer: Language models can teach themselves to use tools
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., and Scialom, T. (2023) · 2023
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