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相关概念视频

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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.
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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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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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相关实验视频

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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对医学中的大型语言模型的对抗性攻击

Yifan Yang1,2, Qiao Jin1, Furong Huang2

  • 1National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.

ArXiv
|October 14, 2024
PubMed
概括

医疗保健中的大型语言模型 (LLM) 容易受到敌对攻击,从而危及患者的安全. 在数据中毒后检测模型重量的变化提供了一个潜在的防御策略.

科学领域:

  • 人工智能在医学中的应用
  • 机器学习安全 机器学习安全
  • 自然语言处理应用程序

背景情况:

  • 大型语言模型 (LLM) 对医疗保健诊断,治疗建议和患者护理的进步有希望.
  • 在敏感的医疗环境中部署LLM受到其易受敌对攻击的威胁,这可能导致患者的不良结果.
  • 为了确保医疗保健中LLM的安全性和可靠性,需要解决它们对操纵的易感性.

研究的目的:

  • 调查开源和专有LLM在三个不同的医疗任务中的敌对攻击的脆弱性.
  • 评估数据中毒对医学应用中的LLM性能和内部模型参数的影响.
  • 确定潜在的方法来检测和减轻对医疗保健LLMs的对抗性攻击.

主要方法:

  • 利用现实世界的患者数据来模拟对LLM的对抗性攻击.
  • 在引入有毒数据之前和之后,对医学基准评估LLM绩效.
  • 分析微调模型权重的变化,以检测恶意操纵的证据.

主要成果:

  • 开源和专有LLM都表现出在多个医疗任务中对对抗性攻击的脆弱性.
  • 毒性数据的整合并没有显著降低标准医疗基准的LLM整体绩效.
  • 观察到微调模型权重的明显变化,表明潜在的对手操纵指标.

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结论:

  • 医疗保健中的LLM容易受到敌对攻击,需要采取强有力的安全措施.
  • 对模型重量转移的分析为检测和抵制对医学LLM的攻击提供了一个有希望的途径.
  • 开发有效的防御机制对于在医疗机构安全可靠地部署LLM至关重要.