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In recent years, large language models (LLMs) have attracted attention due to their ability to generate human-like text.
Some effects of” social desirability” in survey studies
Phillips, D. L. and Clancy, K. J. (1972) · 1972
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Beyond parental control and authoritarian parenting style: Understanding chinese parenting through the cultural notion of training
Chao, R. K. (1994) · 1994
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Culture and systems of thought: holistic versus analytic cognition
Nisbett, R. E., Peng, K., Choi, I., and Norenzayan, A. (2001) · 2001
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Thinking, fast and slow
Kahneman, D. (2011) · 2011
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The rationalizing voter
Lodge, M. (2013) · 2013
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Improving election prediction internationally
Kennedy, R., Wojcik, S., and Lazer, D. (2017) · 2017
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A systematic review of predicting elections based on social media data: research challenges and future directions
Brito, K. D. S., Silva Filho, R. L. C., and Adeodato, P. J. L. (2021) · 2021
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Testing the waters: Behavior across participant pools
Snowberg, E. and Yariv, L. (2021) · 2021
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Using large language models to simulate multiple humans
Aher, G., Arriaga, R., and Kalai, A. (2022) · 2022
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Out of one, many: Using language models to simulate human samples
Argyle, L. P., Busby, E. C., Fulda, N., Gubler, J. R., Rytting, C., and Wingate, D. (2023) · 2023
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Synthetic replacements for human survey data? the perils of large language models
Bisbee, J., Clinton, J. D., Dorff, C., Kenkel, B., and Larson, J. M. (2023) · 2023
Cited alongside, same era.
The shy respondent and propensity to participate in surveys: A proof-of-concept study
Boyle, J., Dayton, J., ZuWallack, R., and Iachan, R. (2023) · 2023
Cited alongside, same era.
Compost: Characterizing and evaluating caricature in llm simulations
Cheng, M., Piccardi, T., and Yang, D. (2023) · 2023
Cited alongside, same era.
From pretraining data to language models to downstream tasks: Tracking the trails of political biases leading to unfair nlp models
Feng, S., Park, C. Y., Liu, Y., and Tsvetkov, Y. (2023) · 2023
Cited alongside, same era.
Employing large language models in survey research
Jansen, B. J., Jung, S.-g., and Salminen, J. (2023) · 2023
Cited alongside, same era.
Evaluating llm-generated topics from survey responses: Identifying challenges in recruiting participants through crowdsourcing
Al Tamime, R., Salminen, J., Jung, S.-G., and Jansen, B. (2024) · 2024
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Predicting results of social science experiments using large language models
Ashokkumar, A., Hewitt, L., Ghezae, I., and Willer, R. (2024) · 2024
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The pollyvote forecast for the 2024 us presidential election
Graefe, A. (2024) · 2024
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Llm-mirror: A generated-persona approach for survey pre-testing
Kim, S., Jeong, J., Han, J. S., and Shin, D. (2024) · 2024
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Interactive continual learning: Fast and slow thinking
Qi, B., Chen, X., Gao, J., Li, D., Liu, J., Wu, L., and Zhou, B. (2024) · 2024
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The problems of llm-generated data in social science research
Rossi, L., Harrison, K., and Shklovski, I. (2024) · 2024
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Kim, J. and Lee, B. (2023) · 2023
Cited alongside, same era.
Investigating emergent goal-like behaviour in large language models using experimental economics
Phelps, S. and Russell, Y. (2023) · 2023
Cited alongside, same era.
Do llms exhibit human-like response biases? a case study in survey design
Tjuatja, L., Chen, V., Wu, S. T., Talwalkar, A., and Neubig, G. (2023) · 2023
Cited alongside, same era.
Exploring large language models for communication games: An empirical study on werewolf
Xu, Y., Wang, S., Li, P., Luo, F., Wang, X., Liu, W., and Liu, Y. (2023) · 2023
Cited alongside, same era.
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Evaluating the moral beliefs encoded in llms
Scherrer, N., Shi, C., Feder, A., and Blei, D. (2024) · 2024
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Can large language model agents simulate human trust behaviors?
Xie, C., Chen, C., Jia, F., Ye, Z., Shu, K., Bibi, A., Hu, Z., Torr, P., Ghanem, B., and Li, G. (2024) · 2024
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Zhang, K., Wang, J., Ding, N., Qi, B., Hua, E., Lv, X., and Zhou, B. (2024) · 2024
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