将个性化广告嵌入大模型聊天机器人，用户竟难以察觉还更喜欢。
Ads that Talk Back: Implications and Perceptions of Injecting Personalized Advertising into LLM Chatbots
- 在大模型回复中动态注入个性化广告，实现隐蔽式营销。
- 179人实验显示广告对响应质量影响极小，用户检测率不足30%。
- 适合关注AI商业化、广告技术与用户体验的研究者阅读。
大型语言模型（LLMs）的进展推动了高效聊天机器人的诞生，但其部署成本引发盈利担忧。本文探索通过广告变现来支撑LLM服务的可行性，设计了一款嵌入个性化产品广告的聊天机器人，基于179名参与者的组间实验进行评估。结果显示，广告注入对模型性能影响微弱，尤其在响应吸引力方面几乎无损；参与者难以识别广告内容，甚至更偏好含隐藏广告的回复。多数用户未点击广告说明，而是尝试用自然语言修改广告设置。研究构建了广告数据集，并开源了经过微调的Phi-4-Ads模型，可灵活适配用户偏好以生成定制化广告内容。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) have enabled the creation of highly effective chatbots. However, the compute costs of widely deploying LLMs have raised questions about profitability. Companies have proposed exploring ad-based revenue streams for monetizing LLMs, which could serve as the new de facto platform for advertising. This paper investigates the implications of personalizing LLM advertisements to individual users via a between-subjects experiment with 179 participants. We developed a chatbot that embeds personalized product advertisements within LLM responses, inspired by similar forays by AI companies. The evaluation of our benchmarks showed that ad injection only slightly impacted LLM performance, particularly response desirability. Results revealed that participants struggled to detect ads, and even preferred LLM responses with hidden advertisements. Rather than clicking on our advertising disclosure, participants tried changing their advertising settings using natural language queries. We created an advertising dataset and an open-source LLM, Phi-4-Ads, fine-tuned to serve ads and flexibly adapt to user preferences.
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