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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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相关实验视频

Updated: Jan 10, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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用大型语言模型模拟人类福祉:在64个国家的64000名个人中进行了系统的验证和误估.

Pat Pataranutaporn1, Nattavudh Powdthavee2, Chayapatr Archiwaranguprok1

  • 1Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139-4307.

Proceedings of the National Academy of Sciences of the United States of America
|November 26, 2025
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概括

大型语言模型 (LLM) 不能准确地预测全球主观幸福感. 它们表现出偏见,特别是在代表性不足的地区,并且未能捕捉到文化深度,这使得它们不适合替代人类自我报告.

关键词:
人工智能的人工智能是人工智能.全球的不平等现象.大型语言模型.生活满意度生活满意度主观的幸福感 的主观的幸福感

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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科学领域:

  • 计算社会科学 计算社会科学
  • 人工智能伦理学 人工智能伦理学

背景情况:

  • 主观福祉对于经济,医疗和政策决策至关重要.
  • 准确地测量幸福感对于做出明智决策至关重要.

研究的目的:

  • 评估大型语言模型 (LLM) 在预测全球主观福祉方面的有效性.
  • 将LLM的绩效与传统的统计模型和人类自我报告进行比较.

主要方法:

  • 在64个国家利用了64000个人的自然语言个人资料.
  • 将四个领先的LLM与自我报告的幸福感和回归模型的预测进行了比较.
  • 进行了预先注册的实验,以评估LLM依赖语言协会与概念理解的依赖.

主要成果:

  • 与回归模型相比,LLM产生了可信的模式,但系统性地表现不佳.
  • 在代表性不足的国家,LLM错误最大,反映了数字和经济不平等.
  • 法律法学表明依赖于表面层面的语言关联,导致在不熟悉的背景下可预测的扭曲.

结论:

  • 法律学士可以模拟生活满意度的广泛相关性,但缺乏经验和文化深度.
  • 简单的生活方式并不能替代人类对幸福感的自我报告.
  • 使用LLM进行福利评估可能会加剧不平等,并削弱人类的行动能力.