Large Language Models (LLMs) are increasingly regarded as having the potential to generate persuasive content at scale. While previous studies have focused on the risks associated with LLM-generated misinformation, the role of LLMs in enabling prosocial persuasion is still underexplored. We investigate whether donation appeals authored by LLMs are as effective as those written by humans across degrees of personalization. Two preregistered online experiments (Study 1: N = 658; Study 2: N = 642) manipulated Personalization (generic vs. personalized vs. falsely personalized) and Content source (human vs. LLM) and presented participants with donation appeals for charities. We assessed how participants distributed their bonus money across the charities, how they engaged with the donation appeals, and how persuasive they found them. In both experiments, LLM-generated content yielded more donations, resulted in higher engagement, and was rated as more persuasive than human-authored content. There was a gain associated with personalization (Study 2) and a penalty for false personalization (Study 1). Our results suggest that LLMs may be a suitable technology for generating content that can encourage prosocial behavior.

翻译：大语言模型（LLM）日益被视为具有规模化生成说服性内容的潜力。以往研究主要关注LLM生成错误信息的风险，而LLM在促进亲社会说服方面的作用仍有待探索。我们研究了LLM生成的捐赠呼吁在个性化程度不同时，是否与人类撰写的呼吁同样有效。两项预注册在线实验（实验1：N=658；实验2：N=642）操纵了个性化程度（通用型 vs. 个性化 vs. 虚假个性化）和内容来源（人类 vs. LLM），并向参与者呈现针对慈善机构的捐赠呼吁。我们评估了参与者如何将奖金分配给各慈善机构、如何与捐赠呼吁互动，以及认为其说服力如何。在两个实验中，LLM生成的内容相比人类撰写的内容获得了更多捐赠、产生了更高的参与度，并被评价为更具说服力。个性化处理（实验2）带来增益，而虚假个性化（实验1）则产生惩罚效应。我们的结果表明，LLM可能是一种适合生成鼓励亲社会行为内容的技术。