1. “Beyond the Buzz: A Look at Large Language Models in Production,” PDF (San Francisco: Predibase, 2023), https://go.predibase.com.
2. A. Rosenbaum, S. Soltan, and W. Hamza, “Using Large Language Models (LLMs) to Synthesize Training Data,” Amazon Science, Jan. 20, 2023, www.amazon.science.
3. “Storm Reply Launches RAG-Based AI Chatbot for Audi, Revolutionising Internal Documentation,” Business Wire, Dec. 21, 2023, www.businesswire.com.
4. “Beyond the Buzz.”
5. P. Vaithilingam, T. Zhang, and E.L. Glassman, “Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models,” in “CHI EA ’22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems,” ed. S. Barbosa, C. Lampe, C. Appert, et al. (New York: Association for Computing Machinery, April 2022), 1-7.
6. S. Noy and W. Zhang, “Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence,” Science 381, no. 6654 (July 13, 2023): 187-192.
7. L. Chen, M. Zaharia, and J. Zou, “How Is ChatGPT’s Behavior Changing Over Time?” arXiv, revised Oct. 21, 2023, https://arxiv.org.
8. S. Ouyang, J.M. Zhang, M. Harman, et al., “LLM Is Like a Box of Chocolates: The Non-Determinism of ChatGPT in Code Generation,” arXiv, submitted Aug. 5, 2023, https://arxiv.org.
9. P. Cappelli, “Stop Overengineering People Management,” Harvard Business Review 98, no. 5 (September-October 2020): 56-63.
10. E. Brynjolfsson, D. Li, and L.R. Raymond, “Generative AI at Work,” working paper 31161, National Bureau of Economic Research, Cambridge, Massachusetts, April 2023. We cannot tell the extent to which the improvement was due to the LLM per se because it was bundled together with an algorithm, which is a different tool.
11. F. Dell’Acqua, E. McFowland III, E. Mollick, et al., “Navigating the Jagged Technological Frontier: Field Experimental Evidence on the Effects of AI on Knowledge Worker Productivity and Quality,” working paper 24-013, Harvard Business School, Boston, September 2023.
12. C.B. Leon, “Occupational Winners and Losers: Who They Were During 1972-80,” Monthly Labor Review 105, no. 6 (June 1982): 18-28.
13. M. Cerullo, “Here’s How Many U.S. Workers ChatGPT Says It Could Replace,” CBS News, April 5, 2023, www.cbsnews.com; and L. Nedelkoska and G. Quintini, “Automation, Skills Use, and Training,” working paper 202, Organization for Economic Cooperation and Development, Paris, March 2018.
14. X. Hui, O. Reshef, and L. Zhou, “The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence From an Online Labor Market,” SSRN, Aug. 1, 2023, https://papers.ssrn.com.
15. J. Liu, X. Xu, Y. Li, et al., “‘Generate’ the Future of Work Through AI: Empirical Evidence From Online Labor Markets,” SSRN, Aug. 3, 2023, https://papers.ssrn.com; and O. Demirci, J. Hannane, and X. Zhu, “Who Is AI Replacing? The Impact of ChatGPT on Online Freelancing Platforms,” SSRN, Oct. 15, 2023, https://papers.ssrn.com.
16. For an example of an acceptable use policy for LLMs, see ACA Global’s template: “Sample Policy: Acceptable Use Policy for Employee Use of Large Language Models on Company Devices,” ACA Aponix, May 2023, https://web.acaglobal.com.
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