Open Collections

Chockkalingam, Shruthi

University of British Columbia

As the use of large language models becomes ever more prevalent, understanding their persuasive abilities, both in ways that can be beneficial and harmful to humans, proves an important task. Previous work has focused on persuasion in the context of negotiations, political debate and advertising. We instead shift the focus to a more realistic setup of a dialogue between a persuadee with an everyday dilemma (e.g., whether to switch to a vegan diet or not) and a persuader with no prior knowledge about the persuadee who is trying to persuade them towards a certain decision based on arguments they feel would be most suited to the persuadee’s persona. We collect and analyze conversations between a human persuadee and either a human persuader or an LLM persuader based on GPT-4. We find that, in this setting, GPT-4 is perceived as both more persuasive and more empathetic, whereas humans are more skilled at discovering new information about the person they are speaking to. This research provides the groundwork for future work predicting the persuasiveness of utterances in conversation across a range of topics.

Text

2025-06-20

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/91377

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/