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关于大型语言模型与网络搜索对学习深度的影响的实验证据

Shiri Melumad¹, Jin Ho Yun^2,3

¹The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA.

|October 30, 2025

概括

从大型语言模型 (LLM) 中学习可能会比传统的网络搜索带来更浅的知识. 这影响了从LLM综合信息中产生的建议的深度和原创性.

科学领域:

认知科学认知科学
人与计算机的交互
信息科学信息科学信息科学

背景情况:

大型语言模型 (LLM) 提供综合信息,与传统的网络搜索结果形成鲜明对比.
基于LLM的学习对知识深度和用户参与的影响需要调查.

研究的目的:

为了比较LLM合成与传统网络搜索的学习深度.
调查LLM生成的摘要如何影响用户随后的咨询生成.

主要方法:

七个在线和实验室实验与10,462名参与者进行.
参与者通过LLM摘要或传统的网络搜索链接了解主题.
测量了随后的咨询产生的质量和知觉的知识深度.

主要成果:

参与者报告说,在从LLM摘要中学习时,即使有增强的网页链接,知识也较浅.
从LLM学习后产生的建议较少,不那么原创,不太可能被采用.
信息的LLM呈现可能会抑制用户的活跃源发现和合成.

结论:

与传统的网络搜索相比,LLM综合可能会培养更浅的知识和更不具影响力的建议.
法学士成果的固有结构可以影响与学习和信息合成相关的认知过程.
需要进一步的研究来探索基于LLM的学习的长期影响和潜在的缓解策略.

关键词:

大型语言模型.学习学习学习学习学习学习自然语言处理自然语言处理.搜索过程搜索过程搜索过程网络搜索搜索网络搜索

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