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This study seeks to identify and quantify biases in simulating political samples with Large Language Models, specifically focusing on vote choice and public opinion.
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M. Madaio, L. Egede, H. Subramonyam, J. Wortman Vaughan, and H. Wallach, “Assessing the fairness of ai systems: Ai practitioners’ processes, challenges, and needs for support,” Proceedings of the ACM on Human-Computer Interaction , vol. 6, no. CSCW1, pp. 1–26, 2022
2022
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L. P. Argyle, E. C. Busby, N. Fulda, J. R. Gubler, C. Rytting, and D. Wingate, “Out of one, many: Using language models to simulate human samples,” Political Analysis , vol. 31, no. 3, pp. 337–351, 2023
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J. J. Horton, “Large language models as simulated economic agents: What can we learn from homo silicus?” National Bureau of Economic Research, Tech. Rep., 2023
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C. Ziems, W. Held, O. Shaikh, J. Chen, Z. Zhang, and D. Yang, “Can large language models transform computational social science?” Computational Linguistics , pp. 1–53, 2023
2023
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G. V. Aher, R. I. Arriaga, and A. T. Kalai, “Using large language models to simulate multiple humans and replicate human subject studies,” in International Conference on Machine Learning . PMLR, 2023, pp. 337–371
2023
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D. Dillion, N. Tandon, Y. Gu, and K. Gray, “Can ai language models replace human participants?” Trends in Cognitive Sciences , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
H. Kotek, R. Dockum, and D. Sun, “Gender bias and stereotypes in large language models,” in Proceedings of The ACM Collective Intelligence Conference , 2023, pp. 12–24
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
F. Huang, H. Kwak, and J. An, “Is chatgpt better than human annotators? potential and limitations of chatgpt in explaining implicit hate speech,” in Companion proceedings of the ACM web conference 2023 , 2023, pp. 294–297
2023
Later among the works it cites.
H. Lyu, S. Jiang, H. Zeng, Y. Xia, Q. Wang, S. Zhang, R. Chen, C. Leung, J. Tang, and J. Luo, “Llm-rec: Personalized recommendation via prompting large language models,” in Findings of the Association for Computational Linguistics: NAACL 2024 , 2024, pp. 583–612
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E. Ferrara, “Fairness and bias in artificial intelligence: A brief survey of sources, impacts, and mitigation strategies,” Sci , vol. 6, no. 1, p. 3, 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2024
Closest in time.
X. Mou, Z. Li, H. Lyu, J. Luo, and Z. Wei, “Unifying local and global knowledge: Empowering large language models as political experts with knowledge graphs,” in Proceedings of the ACM on Web Conference 2024 , 2024, pp. 2603–2614
2024
Closest in time.
2024
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W. Qi, J. Pan, H. Lyu, and J. Luo, “Excitements and concerns in the post-chatgpt era: Deciphering public perception of ai through social media analysis,” Telematics and Informatics , p. 102158, 2024
2024
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K. Miyazaki, T. Murayama, T. Uchiba, J. An, and H. Kwak, “Public perception of generative ai on twitter: an empirical study based on occupation and usage,” EPJ Data Science , vol. 13, no. 1, p. 2, 2024
2024
Closest in time.
2024
Closest in time.
H. Lyu, W. Qi, Z. Wei, and J. Luo, “Human vs. lmms: Exploring the discrepancy in emoji interpretation and usage in digital communication,” in Proceedings of the International AAAI Conference on Web and Social Media , vol. 18, 2024, pp. 2104–2110
2024
Closest in time.