跨学科思维所需的外部信息环境在基于跨学科的简要简要写作的大型语言模型中
Mei Chen1,2, Yijun Su3,4, Junyuan Guo3,4
1School of Information Engineering, Minzu University of China, Beijing, 100081, China. chenmei298@126.com.
Scientific reports
|November 7, 2025
概括
外部信息环境显著影响大语言模型 (LLM) 的跨学科思维. 深入和广泛的知识获取促进了融合思维,而交叉解释在LLMs中增强了分歧思维.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 信息科学 信息科学 信息科学
背景情况:
- 外部信息刺激对人类知识的重新整合和跨学科思维至关重要.
- 了解信息环境如何影响人工智能,特别是大型语言模型 (LLM),对于推进人工智能能力至关重要.
研究的目的:
- 调查外部信息环境对LLM跨学科思维的影响.
- 分析不同信息环境如何影响LLMs产生跨学科的简要概要的能力,评估融合和分歧的思维.
主要方法:
- 利用一般摘要和简要摘要,为LLMs创建深入和广泛的知识背景.
- 在各种信息输入组合下,通过ChatGPT-4.0和Claude3生成的交叉解释和交叉灵感进行比较.
- 通过跨学科的简要概要来评估LLM的融合和分歧思维.
主要成果:
- 同时获得深入和广泛的知识背景,增强了LLM的融合思维.
- 信息环境中的交叉解释在LLMs中培养了不同的思维.
- 在外部信息环境和LLM的不同思维之间观察到温和的相关性.
结论:
- 外部信息环境在塑造LLM跨学科思维能力方面发挥着重要作用.
- 定制信息输入可以优化LLM用于特定的认知任务,如融合和分歧思维.
- 这项研究有助于在跨学科研究和知识合成中推进LLM的应用.
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