Large language models (LLMs) are increasingly being integrated into search engines to provide natural language responses tailored to user queries. Customers and end-users are also becoming more dependent on these models for quick and easy purchase decisions. In this work, we investigate whether recommendations from LLMs can be manipulated to enhance a product's visibility. We demonstrate that adding a strategic text sequence (STS) -- a carefully crafted message -- to a product's information page can significantly increase its likelihood of being listed as the LLM's top recommendation. To understand the impact of STS, we use a catalog of fictitious coffee machines and analyze its effect on two target products: one that seldom appears in the LLM's recommendations and another that usually ranks second. We observe that the strategic text sequence significantly enhances the visibility of both products by increasing their chances of appearing as the top recommendation. This ability to manipulate LLM-generated search responses provides vendors with a considerable competitive advantage and has the potential to disrupt fair market competition. Just as search engine optimization (SEO) revolutionized how webpages are customized to rank higher in search engine results, influencing LLM recommendations could profoundly impact content optimization for AI-driven search services. Code for our experiments is available at https://github.com/aounon/llm-rank-optimizer.

翻译：大型语言模型（LLMs）正日益被集成到搜索引擎中，以提供针对用户查询的自然语言响应。顾客和终端用户也越来越依赖这些模型来快速便捷地做出购买决策。在本研究中，我们探讨了LLMs的推荐是否可以被操纵以增强产品的可见性。我们证明，在产品信息页面添加一个策略性文本序列——一条精心设计的消息——可以显著增加其被列为LLM首要推荐的可能性。为了理解STS的影响，我们使用了一个虚构的咖啡机目录，并分析了其对两个目标产品的影响：一个很少出现在LLM推荐中，另一个通常排名第二。我们观察到，策略性文本序列通过增加两者作为首要推荐出现的机会，显著提升了这两种产品的可见性。这种操纵LLM生成的搜索响应的能力为供应商提供了相当大的竞争优势，并有可能破坏公平的市场竞争。正如搜索引擎优化（SEO）彻底改变了网页如何定制以在搜索引擎结果中获得更高排名一样，影响LLM推荐可能会深刻影响面向AI驱动搜索服务的内容优化。我们的实验代码可在 https://github.com/aounon/llm-rank-optimizer 获取。