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相关概念视频

Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Propagation of Uncertainty from Random Error00:59

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Interpretation of Confidence Intervals01:19

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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简单但有效:一个信息理论方法,多LLM不确定性量化.

Maya Kruse1, Majid Afshar2, Saksham Khatwani1,3

  • 1University of Colorado Anschutz Medical Campus.

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing
|December 16, 2025
PubMed
概括

本研究介绍了MUSE,一种使用多个大型语言模型 (LLM) 改进不确定性估计的方法. 通过汇总不同的LLM输出,MUSE提高了关键应用中的预测可靠性.

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科学领域:

  • 人工智能的人工智能
  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 大型语言模型 (LLM) 显示了依赖输入的不一致性,突出了对稳健不确定性量化的需求.
  • 现有的方法往往侧重于单个模型,忽视利用模型多样性的潜在好处,以提高可靠性.

研究的目的:

  • 通过利用模型多样性,提出和评估MUSE (通过子集集的多LLM不确定性),这是通过利用模型多样性来量化LLM不确定性的新方法.
  • 为了证明汇总来自不同LLM的输出,与单个模型相比,可以产生更可靠的不确定性估计.

主要方法:

  • MUSE采用信息理论方法,特别是Jensen-Shannon分歧,以识别和汇总精确校准的LLMs子集.
  • 该方法利用LLM的假设补充预测,因为培训数据的变化和语言的Zipfian性质.

主要成果:

  • 对二进制预测任务的实验表明,MUSE比单模型和基本集合方法显著改善校准和预测性能.
  • 这项研究验证了这样一个假设,即不同的LLM输出,当有效汇总时,会导致优异的不确定性量化.

结论:

  • MUSE为提高大型语言模型的可靠性和校准提供了一种有效的策略,特别是在高风险的场景中.
  • 拟议的方法为进一步研究人工智能系统中不确定性量化的合体技术提供了基础.