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相关概念视频

Motivational Bias01:25

Motivational Bias

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Cognitive bias results from limitations in thinking and information processing, leading to systematic errors in judgment. Conversely, motivational bias stems from personal desires or emotions, causing distortions in perception to align with self-interest. Motivational bias influences how individuals perceive and attribute causes to events, often shaped by personal needs, goals, and self-esteem preservation. This bias can distort judgment, leading to inaccurate assessments of success, failure,...
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Language and Cognition01:27

Language and Cognition

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Self-Evaluation Maintenance Model01:29

Self-Evaluation Maintenance Model

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The Self-Evaluation Maintenance (SEM) model offers a psychological framework to understand how individuals’ self-esteem is influenced by the achievements of others, particularly those with whom they share close personal bonds. The SEM model operates when personal rather than social identity guides individuals. Central to this model is the notion that individuals have an inherent desire to preserve a favorable self-image, which is continuously shaped by interpersonal comparisons and...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Qualitative analysis is the process of identifying elements, ions, or compounds in an unknown sample. It is the first and most fundamental type of analysis based on the hierarchy of analytical goals. This hierarchy is significant as it provides a structured approach to scientific research, with qualitative analysis serving as the initial step, providing essential information before moving on to quantitative or other forms of analysis.
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相关实验视频

Updated: Jan 16, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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使用大型语言模型和隐藏的马尔科夫模型评估激励面试质量.

Kyungho Lim1,2, Young-Chul Jung3,4, Byung-Hoon Kim5,6,7,8

  • 1Department of Psychiatry, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.

BMC psychiatry
|October 2, 2025
PubMed
概括

本研究引入了一种使用大语言模型 (LLM) 和隐藏马尔科夫模型 (HMM) 的自动化框架,以客观地评估动机面试 (MI) 质量. 该LLM-HMM框架准确地预测会话质量,并揭示了有效的MI中明显的动机状态过渡.

关键词:
隐藏的马尔科夫模型面试分析 面试分析面试质量评估 面试质量评估大型语言模型.激励性面试 激励性面试

相关实验视频

Last Updated: Jan 16, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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科学领域:

  • 行为科学 行为科学
  • 计算语言学 计算语言学
  • 人工智能的人工智能

背景情况:

  • 激励面试 (MI) 是一种辅导方法,以促进行为改变.
  • 传统的MI质量评估是劳动密集型和主观的.
  • 建议使用LLM和HMM的自动化框架进行MI评估.

研究的目的:

  • 评估一个LLM-HMM框架来预测MI会话质量.
  • 在高质量和低质量的MI会议中检查动机状态过渡.

主要方法:

  • 分析了40个MI会议,用LLM对客户发言进行分类.
  • 使用HMM来建模基于LLM成绩的动机状态过渡.
  • 过渡矩阵的量化差异和使用LOOCV评估的预测性能.

主要成果:

  • 高质量的MI会议显示了流动的动机状态过渡;低质量的会议表现出持久的抵抗.
  • 在会话质量组之间发现过渡矩阵的统计学上显著差异 (p < 0.001).
  • 通过LOOCV,LLM-HMM框架在预测MI会话质量方面实现了0.80准确度.

结论:

  • 该LLM-HMM框架提供了一个可扩展和客观的替代手动MI质量评估.
  • 潜在的未来应用包括实时治疗师支持,培训和预后预测.
  • 建议对现场收集的数据进行进一步验证.