Mind What You Ask For: Emotional and Rational Faces of Persuasion by Large Language Models
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866929715270385664 |
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| author | Mieleszczenko-Kowszewicz, Wiktoria Bajcar, Beata Babiak, Jolanta Dyczek, Berenika Świstak, Jakub Biecek, Przemysław |
| author_facet | Mieleszczenko-Kowszewicz, Wiktoria Bajcar, Beata Babiak, Jolanta Dyczek, Berenika Świstak, Jakub Biecek, Przemysław |
| contents | Be careful what you ask for, you just might get it. This saying fits with the way large language models (LLMs) are trained, which, instead of being rewarded for correctness, are increasingly rewarded for pleasing the recipient. So, they are increasingly effective at persuading us that their answers are valuable. But what tricks do they use in this persuasion? In this study, we examine what are the psycholinguistic features of the responses used by twelve different language models. By grouping response content according to rational or emotional prompts and exploring social influence principles employed by LLMs, we ask whether and how we can mitigate the risks of LLM-driven mass misinformation. We position this study within the broader discourse on human-centred AI, emphasizing the need for interdisciplinary approaches to mitigate cognitive and societal risks posed by persuasive AI responses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09687 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mind What You Ask For: Emotional and Rational Faces of Persuasion by Large Language Models Mieleszczenko-Kowszewicz, Wiktoria Bajcar, Beata Babiak, Jolanta Dyczek, Berenika Świstak, Jakub Biecek, Przemysław Computation and Language Artificial Intelligence Human-Computer Interaction Be careful what you ask for, you just might get it. This saying fits with the way large language models (LLMs) are trained, which, instead of being rewarded for correctness, are increasingly rewarded for pleasing the recipient. So, they are increasingly effective at persuading us that their answers are valuable. But what tricks do they use in this persuasion? In this study, we examine what are the psycholinguistic features of the responses used by twelve different language models. By grouping response content according to rational or emotional prompts and exploring social influence principles employed by LLMs, we ask whether and how we can mitigate the risks of LLM-driven mass misinformation. We position this study within the broader discourse on human-centred AI, emphasizing the need for interdisciplinary approaches to mitigate cognitive and societal risks posed by persuasive AI responses. |
| title | Mind What You Ask For: Emotional and Rational Faces of Persuasion by Large Language Models |
| topic | Computation and Language Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2502.09687 |