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Updated: Jun 26, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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比较和评估四个人工智能聊天机器人的经济学能力.

Patrik T Hultberg1, David Santandreu Calonge2, Firuz Kamalov3

  • 1Department of Economics and Business, Kalamazoo College, Kalamazoo, Michigan, United States of America.

PloS one
|May 8, 2024
PubMed
概括

像GPT-3.5,GPT-4,Bard和LLaMA 2这样的人工智能 (AI) 聊天机器人在经济学中显示出不同的准确性. 性能差异随着迅速复杂性的增加而增加,影响学术使用.

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科学领域:

  • 经济学 经济学 经济学
  • 人工智能的人工智能
  • 教育技术的教育技术.

背景情况:

  • 人工智能聊天机器人提供学术支持,但具有可变的准确性.
  • 人工智能模型之间存在响应准确度和解释质量的差异.
  • 了解AI聊天机器人的功能对于学术诚信和学习至关重要.

研究的目的:

  • 评估四个人工智能聊天机器人的准确性和解释质量 (GPT-3.5,GPT-4,Bard,LLaMA 2) 在大学级经济学中.
  • 使用Bloom的分类学来比较不同级别的认知复杂性的聊天机器人性能.
  • 为了确定人工智能聊天机器人的性能是否随着经济提示复杂性的增加而显著变化.

主要方法:

  • 使用标准的经济学测试和高级问题进行大学水平的评估.
  • 应用了布鲁姆的分类学来结构提示和评估认知复杂性.
  • 对比了四个不同的AI聊天机器人的结论准确度和解释质量.

主要成果:

  • 同等性能的零假设被拒绝,表明聊天机器人之间存在显著差异.
  • 人工智能聊天机器人的表现在准确性和经济学解释质量方面显著差异.
  • 随着经济学要求的复杂性增加,绩效差异扩大.

结论:

  • 人工智能聊天机器人在经济学教育中表现出显著的性能差异.
  • 快速复杂性是影响人工智能聊天机器人的有效性的关键因素.
  • 调查结果为学生在课程设计中选择适当的AI工具和教育工作者提供了信息.