The advent of large language models (LLMs) has ushered in a new paradigm of
search engines that use generative models to gather and summarize information
to answer user queries. This emerging technology, which we formalize under the
unified framework of Generative Engines (GEs), has the potential to generate
accurate and personalized responses, and is rapidly replacing traditional
search engines like Google and Bing. Generative Engines typically satisfy
queries by synthesizing information from multiple sources and summarizing them
with the help of LLMs. While this shift significantly improves \textit{user}
utility and \textit{generative search engine} traffic, it results in a huge
challenge for the third stakeholder -- website and content creators. Given the
black-box and fast-moving nature of Generative Engines, content creators have
little to no control over when and how their content is displayed. With
generative engines here to stay, the right tools should be provided to ensure
that creator economy is not severely disadvantaged. To address this, we
introduce Generative Engine Optimization (GEO), a novel paradigm to aid content
creators in improving the visibility of their content in Generative Engine
responses through a black-box optimization framework for optimizing and
defining visibility metrics. We facilitate systematic evaluation in this new
paradigm by introducing GEO-bench, a benchmark of diverse user queries across
multiple domains, coupled with sources required to answer these queries.
Through rigorous evaluation, we show that GEO can boost visibility by up to
40\% in generative engine responses. Moreover, we show the efficacy of these
strategies varies across domains, underscoring the need for domain-specific
methods. Our work opens a new frontier in the field of information discovery
systems, with profound implications for generative engines and content
creators.

大型语言模型的出现引入了一种新的搜索引擎范式，使用生成模型来收集和总结信息以回答用户查询。我们正式将这种新兴技术统一归类为生成引擎，并认为它具有生成准确和个性化的回答的潜力，正在快速取代谷歌和必应等传统搜索引擎。为了解决内容创作者面临的挑战，我们引入了生成引擎优化（GEO），这是一种通过黑盒优化框架来优化和定义可见性指标，以帮助内容创作者提高其内容在生成引擎回应中的可见性，且经过严格评估，我们展示了 GEO 在生成引擎回应中可提高可见性高达 40％的效果，同时也强调了对特定领域的需求。我们的工作在信息发现系统领域开辟了新的前沿，并对生成引擎和内容创作者产生了深远的影响。

GEO: 生成引擎优化

GEO: Generative Engine Optimization

Recent advances in large language models (LLMs), such as ChatGPT, have led to
highly sophisticated conversation agents. However, these models suffer from
"hallucinations," where the model generates false or fabricated information.
Addressing this challenge is crucial, particularly with AI-driven platforms
being adopted across various sectors. In this paper, we propose a novel method
to recognize and flag instances when LLMs perform outside their domain
knowledge, and ensuring users receive accurate information.
We find that the use of context combined with embedded tags can successfully
combat hallucinations within generative language models. To do this, we
baseline hallucination frequency in no-context prompt-response pairs using
generated URLs as easily-tested indicators of fabricated data. We observed a
significant reduction in overall hallucination when context was supplied along
with question prompts for tested generative engines. Lastly, we evaluated how
placing tags within contexts impacted model responses and were able to
eliminate hallucinations in responses with 98.88% effectiveness.

本文提出了一种新颖的方法来识别和标记 LLMs perform out of knowledge 的情况，并确保用户获得准确的信息。通过使用上下文和嵌入标签，可以成功地消除生成语言模型中的幻觉。在对测试生成引擎的问题提示进行上下文基线幻觉频率时，观察到了整体幻觉的显着减少。