Columbia Business School, New York, USA.
Center for Advanced Technology and Human Performance, Columbia Business School, New York, USA.
Sci Rep. 2024 Feb 26;14(1):4692. doi: 10.1038/s41598-024-53755-0.
Matching the language or content of a message to the psychological profile of its recipient (known as "personalized persuasion") is widely considered to be one of the most effective messaging strategies. We demonstrate that the rapid advances in large language models (LLMs), like ChatGPT, could accelerate this influence by making personalized persuasion scalable. Across four studies (consisting of seven sub-studies; total N = 1788), we show that personalized messages crafted by ChatGPT exhibit significantly more influence than non-personalized messages. This was true across different domains of persuasion (e.g., marketing of consumer products, political appeals for climate action), psychological profiles (e.g., personality traits, political ideology, moral foundations), and when only providing the LLM with a single, short prompt naming or describing the targeted psychological dimension. Thus, our findings are among the first to demonstrate the potential for LLMs to automate, and thereby scale, the use of personalized persuasion in ways that enhance its effectiveness and efficiency. We discuss the implications for researchers, practitioners, and the general public.
将信息的语言或内容与接收者的心理特征相匹配(称为“个性化说服”)被广泛认为是最有效的信息传递策略之一。我们证明,像 ChatGPT 这样的大型语言模型的快速发展可以通过使个性化说服规模化来加速这种影响。在四项研究(包括七个子研究;总 N=1788)中,我们表明 ChatGPT 生成的个性化信息比非个性化信息具有更显著的影响力。这在不同的说服领域(例如,消费品营销、气候行动的政治呼吁)、心理特征(例如,个性特征、政治意识形态、道德基础)以及仅向语言模型提供一个简短的提示来命名或描述目标心理维度时都是如此。因此,我们的发现是首批证明大型语言模型有可能自动化、规模化地使用个性化说服的研究之一,从而提高其有效性和效率。我们讨论了这些发现对研究人员、从业者和公众的影响。