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Large Language Models (LLMs) are reshaping consumer decision-making, particularly in communication with firms, yet our understanding of their impact remains limited.
Probing neural network comprehension of natural language arguments
Niven, T. and H.-Y. Kao (2019) · 1907
Earlier work this paper cites.
Binary codes capable of correcting deletions, insertions, and reversals
Levenshtein, V. I. (1966) · 1965
Earlier work this paper cites.
Accommodation theory: Optimal levels of convergence
Giles, H. (1979) · 1979
Earlier work this paper cites.
Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling
Terza, J. V., A. Basu, and P. J. Rathouz (2008) · 2008
Earlier work this paper cites.
Mostly harmless econometrics: An empiricist’s companion
Angrist, J. D. and J.-S. Pischke (2009) · 2009
Earlier work this paper cites.
The wrong side (s) of the tracks: The causal effects of racial segregation on urban poverty and inequality
Ananat, E. O. (2011) · 2011
Earlier work this paper cites.
The slave trade and the origins of mistrust in africa
Nunn, N. and L. Wantchekon (2011) · 2011
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Choosing models for health care cost analyses: issues of nonlinearity and endogeneity
Garrido, M. M., P. Deb, J. F. Burgess Jr, and J. D. Penrod (2012) · 2012
Earlier work this paper cites.
Financial literacy, financial education, and downstream financial behaviors
Fernandes, D., J. G. Lynch Jr, and R. G. Netemeyer (2014) · 2014
Earlier work this paper cites.
The economic importance of financial literacy: Theory and evidence
Lusardi, A. and O. S. Mitchell (2014) · 2014
Earlier work this paper cites.
Control function methods in applied econometrics
Wooldridge, J. M. (2015) · 2015
Earlier work this paper cites.
2sls versus 2sri: A ppropriate methods for rare outcomes and/or rare exposures
Basu, A., N. B. Coe, and C. G. Chapman (2018) · 2018
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It’s not just what you say, but how you say it: The effect of language style matching on perceived quality of consumer reviews
Liu, A. X., Y. Xie, and J. Zhang (2019) · 2019
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Emergent linguistic structure in artificial neural networks trained by self-supervision
Manning, C. D., K. Clark, J. Hewitt, U. Khandelwal, and O. Levy (2020) · 2020
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Capacity pooling in hospitals: The hidden consequences of off-service placement
Song, H., A. L. Tucker, R. Graue, S. Moravick, and J. J. Yang (2020) · 2020
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The legacy of colonial medicine in central africa
Lowes, S. and E. Montero (2021) · 2021
Cited alongside, same era.
Beyond fake or genuine–the effect of large language models (llms) on the content and sentiment of product reviews
Ma, L. and L. Luo (2023) · 2023
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Most americans haven’t used chatgpt; few think it will have a major impact on their job
Park, E. and R. Gelles-Watnick (2023, 8) · 2023
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‘Chatgpt detector’ catches AI-generated papers with unprecedented accuracy
Prillaman, M. (2023) · 2023
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Verbosity bias in preference labeling by large language models
Saito, K., A. Wachi, K. Wataoka, and Y. Akimoto (2023) · 2023
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Let’s have a chat! a conversation with chatgpt: Technology, applications, and limitations
Shahriar, S. and K. Hayawi (2023) · 2023
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Chen, J., W. Fan, J. Wei, and Z. Liu (2022) · 2022
Cited alongside, same era.
Autoformalization with large language models
Wu, Y., A. Q. Jiang, W. Li, M. Rabe, C. Staats, M. Jamnik, and C. Szegedy (2022) · 2022
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Learning from peers: Evidence from disclosure of consumer complaints
Dou, Y., M. Hung, G. She, and L. L. Wang (2023) · 2023
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Ai content detector accuracy review + open source dataset and research tool
Gillham, J. (2023) · 2023
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Deploying artificial intelligence in services to aid vulnerable consumers
Hermann, E., G. Y. Williams, and S. Puntoni (2023) · 2023
Cited alongside, same era.
Weber-Wulff, D., A. Anohina-Naumeca, S. Bjelobaba, T. Foltỳnek, J. Guerrero-Dib, O. Popoola, P. Šigut, and L. Waddington (2023) · 2023
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The rapid adoption of generative ai
Bick, A., A. Blandin, and D. J. Deming (2024, September) · 2024
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Mapping the increasing use of llms in scientific papers
Liang, W., Y. Zhang, Z. Wu, H. Lepp, W. Ji, X. Zhao, H. Cao, S. Liu, S. He, Z. Huang, et al. (2024) · 2024
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Americans’ use of chatgpt is ticking up, but few trust its election information
McClain, C. (2024, 3) · 2024
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The unequal adoption of chatgpt exacerbates existing inequalities among workers
Humlum, A. and E. Vestergaard (2025) · 2025
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