比较生成式AI与网络搜索在知识获取中的优劣，发现各适于不同场景。
Generative AI's aggregated knowledge versus web-based curated knowledge
- 对比GenAI与谷歌搜索，测试不同知识需求下的表现差异。
- 生成式AI擅长整合广为人知的通用知识，提升决策效率。
- 传统搜索更适合精准、小众知识查询，验证事实更可靠。
本文研究生成式AI（基于大语言模型）聚合知识与传统网络搜寻知识在不同问题上的适用性。通过对比ChatGPT、Google搜索及两者结合进行产品搜索实验，发现生成式AI能加速对广泛主题的探索与决策；而传统搜索在验证事实、逻辑和上下文方面更具深度。实验表明，对于具体、冷门知识，网络溯源搜索价值更高；而对于常见主题，生成式AI更善于整合多源信息。研究提出一个分类框架，帮助区分用户在何种目标下应选择哪种知识获取方式。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。