生成式搜索引擎的偏好与网页内容协同优化策略

摘要

通过运用大型语言模型（LLMs）检索文档并生成自然语言响应，生成引擎如Google AI概览和ChatGPT，极大地提升了用户体验，并迅速成为搜索的新形态。它们的快速普及也催生了生成引擎优化（GEO）的需求，内容提供商渴望从中获得更多关注。本文介绍了AutoGEO，一个框架，旨在自动学习生成引擎在使用检索内容生成响应时的偏好，并重写网页内容以增加此类关注。AutoGEO首先引导前沿LLMs解释生成引擎偏好，并从这些解释中提取有意义的偏好规则。随后，它将这些偏好规则作为AutoGEO_API（一个基于提示的GEO系统）的上下文工程，以及作为基于规则的奖励来训练AutoGEO_Mini，一个成本效益高的GEO模型。在标准GEO-Bench及两个新构建的、使用真实用户查询的基准测试上的实验，证明了AutoGEO在增强内容关注度的同时保持搜索效用的有效性。分析确认了所学规则的鲁棒性及其捕捉不同领域独特偏好的能力，以及AutoGEO系统在内容优化中嵌入这些规则的能力。代码已发布于https://github.com/cxcscmu/AutoGEO。

English

By employing large language models (LLMs) to retrieve documents and generate natural language responses, Generative Engines, such as Google AI overview and ChatGPT, provide significantly enhanced user experiences and have rapidly become the new form of search. Their rapid adoption also drives the needs of Generative Engine Optimization (GEO), as content providers are eager to gain more traction from them. In this paper, we introduce AutoGEO, a framework to automatically learn generative engine preferences when using retrieved contents for response generation, and rewrite web contents for more such traction. AutoGEO first prompts frontier LLMs to explain generative engine preferences and extract meaningful preference rules from these explanations. Then it uses preference rules as context engineering for AutoGEO_API, a prompt-based GEO system, and as rule-based rewards to train AutoGEO_Mini, a cost-effective GEO model. Experiments on the standard GEO-Bench and two newly constructed benchmarks using real user queries demonstrate the effectiveness of AutoGEO in enhancing content traction while preserving search utility. Analyses confirm the learned rules' robustness and abilities to capture unique preferences in variant domains, and AutoGEO systems' ability to embed them in content optimization. The code is released at https://github.com/cxcscmu/AutoGEO.

生成式搜索引擎的偏好与网页内容协同优化策略

What Generative Search Engines Like and How to Optimize Web Content Cooperatively

摘要

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