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相关概念视频

Purposive Learning01:22

Purposive Learning

426
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
426

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相关实验视频

Updated: Jan 11, 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

1000

强化学习用于基于LLM的可解释的TCM处方建议,并从小语言模型中隐含偏好.

Xinyu Wang1,2,3, Xiaohe Sun4, Lei Yang1

  • 1School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, China.

Chinese medicine
|November 19, 2025
PubMed
概括

这项研究引入了传统中医药 (TCM) 处方建议的新两阶段框架,提高了准确性和解释性. 该方法利用知识蒸和强化学习来改善临床决策支持.

关键词:
巴特·巴特 (BART BART) 是一个著名的艺术家.直接偏好优化优化直接偏好优化隐式偏好是一种隐式偏好.知识的蒸知识的蒸.关于TCM的处方建议

更多相关视频

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

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

Published on: June 13, 2025

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相关实验视频

Last Updated: Jan 11, 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

1000
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

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

Published on: June 13, 2025

1.3K

科学领域:

  • 人工智能的人工智能
  • 计算医学是一种计算医学.
  • 传统中国医药 传统中国医药

背景情况:

  • 传统中医 (TCM) 处方建议需要提高可解释性和准确性.
  • 现有的智能决策支持系统通常在建议中缺乏透明度.

研究的目的:

  • 开发一个两阶段的培训框架,以提高TCM处方建议的解释性和准确性.
  • 将知识蒸和隐性偏好驱动的强化学习整合到一个紧的模型中.

主要方法:

  • 使用GPT-4o分析TCM临床记录的蒸样本.
  • 使用低级调整 (LoRA) 来微调Qwen2.5-7B模型以产生可解释的输出.
  • 训练了一种轻量级的BART模型,并使用直接偏好优化 (DPO) 来进行强化调整.

主要成果:

  • 实现了35.62%的P@30和37.36%的F1@30,表现优于基线.
  • 知识蒸提高了概括性和可解释性.
  • 强化学习进一步提高了F1@30的2.01%.

结论:

  • 拟议的方法提高了TCM处方建议的质量和透明度.
  • 提供了构建可靠和临床适用的智能TCM决策支持系统的策略.