LLM生成的广告：从个性化平局到说服优势
LLM-Generated Ads: From Personalization Parity to Persuasion Superiority
AI总结：
本文研究了LLM生成广告在个性化与说服力上的表现，发现其在说服策略上优于人类，尤其在权威和共识原则中表现突出。
摘要：
随着大语言模型（LLMs）越来越能够生成有说服力的内容，理解它们在不同广告策略下的有效性变得至关重要。本文通过互补的视角对LLM生成的广告进行了两方面的研究：（1）基于个性的和（2）基于心理说服原则的。在我们的第一项研究（n=400）中，我们测试了LLMs能否生成针对特定个性特征（开放性和神经质）的个性化广告，以及它们的表现如何与人类专家相比。结果显示，LLM生成的广告在统计上与人类撰写的广告持平（51.1% vs. 48.9%，p > 0.05），在匹配个性的情况下没有显著表现差异。在这些见解的基础上，我们的第二项研究（n=800）从个体个性化转向普遍说服，测试LLM在四个基础心理原则上的表现：权威、共识、认知和稀缺性。AI生成的广告在人类创作的内容上表现显著更好，达到59.1%的偏好率（vs. 40.9%，p < 0.001），在权威（63.0%）和共识（62.5%）的呼吁中表现最强。定性分析显示AI的优势源于创作更复杂、有抱负的信息，并实现更优越的视觉-叙事一致性。关键的是，这种质量优势证明是稳健的：即使在参与者正确识别AI来源时应用21.2个百分点的检测惩罚，AI广告仍然优于人类广告，且29.4%的参与者在知道其来源的情况下选择了AI内容。这些发现证明了LLMs从个性化平局到说服叙事优势的演变，鉴于LLMs接近零的边际成本和时间要求，相比人类专家，对广告实践有重要意义。
英文摘要：
As large language models (LLMs) become increasingly capable of generating persuasive content, understanding their effectiveness across different advertising strategies becomes critical. This paper presents a two-part investigation examining LLM-generated advertising through complementary lenses: (1) personality-based and (2) psychological persuasion principles. In our first study (n=400), we tested whether LLMs could generate personalized advertisements tailored to specific personality traits (openness and neuroticism) and how their performance compared to human experts. Results showed that LLM-generated ads achieved statistical parity with human-written ads (51.1% vs. 48.9%, p > 0.05), with no significant performance differences for matched personalities. Building on these insights, our second study (n=800) shifted focus from individual personalization to universal persuasion, testing LLM performance across four foundational psychological principles: authority, consensus, cognition, and scarcity. AI-generated ads significantly outperformed human-created content, achieving a 59.1% preference rate (vs. 40.9%, p < 0.001), with the strongest performance in authority (63.0%) and consensus (62.5%) appeals. Qualitative analysis revealed AI's advantage stems from crafting more sophisticated, aspirational messages and achieving superior visual-narrative coherence. Critically, this quality advantage proved robust: even after applying a 21.2 percentage point detection penalty when participants correctly identified AI-origin, AI ads still outperformed human ads, and 29.4% of participants chose AI content despite knowing its origin. These findings demonstrate LLMs' evolution from parity in personalization to superiority in persuasive storytelling, with significant implications for advertising practice given LLMs' near-zero marginal cost and time requirements compared to human experts.
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。
