his paper explores what kinds of questions are best served by the way generative AI (GenAI) using Large Language Models(LLMs) that aggregate and package knowledge, and when traditional curated web-sourced search results serve users better. An experiment compared product searches using ChatGPT, Google search engine, or both helped us understand more about the compelling nature of generated responses. The experiment showed GenAI can speed up some explorations and decisions. We describe how search can deepen the testing of facts, logic, and context. We show where existing and emerging knowledge paradigms can help knowledge exploration in different ways. Experimenting with searches, our probes showed the value for curated web search provides for very specific, less popularly-known knowledge. GenAI excelled at bringing together knowledge for broad, relatively well-known topics. The value of curated and aggregated knowledge for different kinds of knowledge reflected in different user goals. We developed a taxonomy to distinguishing when users are best served by these two approaches.

翻译：本文探讨了哪些类型的问题最适合由使用大型语言模型（LLMs）进行知识聚合与整合的生成式人工智能（GenAI）来处理，以及何时传统的网络策展式搜索结果能为用户提供更好的服务。一项实验比较了使用ChatGPT、谷歌搜索引擎或两者结合进行产品搜索的效果，帮助我们进一步理解生成式回答的吸引力本质。实验表明，GenAI能够加速某些探索和决策过程。我们描述了搜索如何能深化对事实、逻辑和语境的检验。我们展示了现有及新兴的知识范式如何以不同方式助力知识探索。通过搜索实验，我们的探究表明，针对非常具体、知名度较低的知识，网络策展式搜索具有重要价值。GenAI则在整合相对广为人知的主题知识方面表现出色。策展式知识与聚合式知识对于不同类型知识的价值，反映了用户目标的差异。我们构建了一个分类体系，用以区分在何种情况下这两种方法能最好地服务于用户。