- Author
- Alexander Rogiers (UGent) , Sander Noels (UGent) , Maarten Buyl (UGent) and Tijl De Bie (UGent)
- Organization
- Project
- Abstract
- The rapid rise of Large Language Models (LLMs) has created new disruptive possibilities for persuasive communication, by enabling fully-automated personalized and interactive content generation at an unprecedented scale. In this paper, we survey the research field of LLM-based persuasion that has emerged as a result. We begin by exploring the different modes in which LLM Systems are used to influence human attitudes and behaviors. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. We identify key factors influencing their effectiveness, such as the manner of personalization and whether the content is labelled as AI-generated. We also summarize the experimental designs that have been used to evaluate progress. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks, including the spread of misinformation, the magnification of biases, and the invasion of privacy. These risks underscore the urgent need for ethical guidelines and updated regulatory frameworks to avoid the widespread deployment of irresponsible and harmful LLM Systems.
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01JD4M72P2722T174X0YZHBB0B
- MLA
- Rogiers, Alexander, et al. “Persuasion with Large Language Models : A Survey.” ARXIV, 2024, doi:10.48550/arXiv.2411.06837.
- APA
- Rogiers, A., Noels, S., Buyl, M., & De Bie, T. (2024). Persuasion with large language models : a survey. https://doi.org/10.48550/arXiv.2411.06837
- Chicago author-date
- Rogiers, Alexander, Sander Noels, Maarten Buyl, and Tijl De Bie. 2024. “Persuasion with Large Language Models : A Survey.” ARXIV. https://doi.org/10.48550/arXiv.2411.06837.
- Chicago author-date (all authors)
- Rogiers, Alexander, Sander Noels, Maarten Buyl, and Tijl De Bie. 2024. “Persuasion with Large Language Models : A Survey.” ARXIV. doi:10.48550/arXiv.2411.06837.
- Vancouver
- 1.Rogiers A, Noels S, Buyl M, De Bie T. Persuasion with large language models : a survey. ARXIV. 2024.
- IEEE
- [1]A. Rogiers, S. Noels, M. Buyl, and T. De Bie, “Persuasion with large language models : a survey,” ARXIV. 2024.
@misc{01JD4M72P2722T174X0YZHBB0B, abstract = {{The rapid rise of Large Language Models (LLMs) has created new disruptive possibilities for persuasive communication, by enabling fully-automated personalized and interactive content generation at an unprecedented scale. In this paper, we survey the research field of LLM-based persuasion that has emerged as a result. We begin by exploring the different modes in which LLM Systems are used to influence human attitudes and behaviors. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. We identify key factors influencing their effectiveness, such as the manner of personalization and whether the content is labelled as AI-generated. We also summarize the experimental designs that have been used to evaluate progress. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks, including the spread of misinformation, the magnification of biases, and the invasion of privacy. These risks underscore the urgent need for ethical guidelines and updated regulatory frameworks to avoid the widespread deployment of irresponsible and harmful LLM Systems.}}, author = {{Rogiers, Alexander and Noels, Sander and Buyl, Maarten and De Bie, Tijl}}, language = {{eng}}, pages = {{16}}, series = {{ARXIV}}, title = {{Persuasion with large language models : a survey}}, url = {{http://doi.org/10.48550/arXiv.2411.06837}}, year = {{2024}}, }
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