大规模的亲社会说服？大语言模型在不同个性化水平上的捐赠呼吁中优于人类
Prosocial Persuasion at Scale? Large Language Models Outperform Humans in Donation Appeals Across Levels of Personalization
AI总结：
研究探讨大语言模型生成的捐赠呼吁在不同个性化水平下的有效性，发现其在捐款数额、参与度和说服力上均优于人类创作内容，但虚假个性化会带来负面影响。
摘要：
大型语言模型（LLMs）日益被视为能够大规模生成说服性内容的潜力。尽管之前的研究所关注LLM生成的虚假信息风险，但LLM在促进亲社会说服中的作用仍被忽视。我们研究了由LLM撰写的捐赠呼吁在不同个性化程度下的有效性。两项预注册在线实验（研究1：N=658；研究2：N=642）操纵了个性化（通用vs.个性化vs.虚假个性化）和内容来源（人类vs.LLM），并展示了慈善机构的捐赠呼吁。我们评估了参与者如何分配奖金给慈善机构，如何与捐赠呼吁互动，以及他们认为这些呼吁有多有说服力。在两项实验中，LLM生成的内容产生了更多的捐款，导致更高的参与度，并被评价为更具说服力。在研究2中，个性化带来了收益，在研究1中，虚假个性化带来了惩罚。我们的结果表明，LLM可能是一种适合生成促进亲社会行为内容的技术。
英文摘要：
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 statistically significantly higher donation amounts than human-authored content, although the absolute differences were small; it also resulted in higher engagement and was rated as more persuasive. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
