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Related Concept Videos

Confidence Coefficient01:24

Confidence Coefficient

The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under both the...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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 't,' or...
Confidence Intervals01:21

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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.
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The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000...
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Distribution Reliability and Automation

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Related Experiment Videos

Adaptive confidence ensemble reranking for reliable knowledge-intensive question answering.

Tanzila Kehkashan1,2, Maha Abdelhaq3, Muhammad Abdullah4

  • 1Faculty of Computing, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia.

Scientific Reports
|June 3, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive ensemble reranking framework for question answering systems. It improves accuracy and efficiency by dynamically adjusting model contributions, outperforming existing methods on benchmark datasets.

Keywords:
Adaptive confidence weightingAnswer rerankingCross-encoder ensembleQuestion answering systemsTransformer models

Related Experiment Videos

Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Information Retrieval

Background:

  • Modern question answering systems use neural networks but struggle with linguistic diversity and computational complexity.
  • Existing reranking systems lack adaptive mechanisms for dynamic model adjustment, limiting effectiveness in knowledge-intensive tasks.

Purpose of the Study:

  • To propose an adaptive confidence ensemble reranking framework for enhanced reliability and efficiency in knowledge-intensive question answering.
  • To address limitations of single-model and traditional ensemble approaches in handling diverse queries and computational demands.

Main Methods:

  • Integration of dual-encoder retrieval with a cross-encoder ensemble of BERT, RoBERTa, and DeBERTa models.
  • Implementation of an instance-aware adaptive confidence weighting mechanism using cross-entropy-based evaluation.
  • Dynamic adjustment of model contributions to optimize answer ranking performance while maintaining computational feasibility.

Main Results:

  • Significant performance improvements across multiple benchmark datasets.
  • A 9.3% increase in mean average precision on MS MARCO.
  • A 2.1% gain on WikiQA and a 1.2% improvement on TREC-QA.

Conclusions:

  • The proposed adaptive confidence ensemble reranking framework effectively enhances reliability and accuracy in knowledge-intensive question answering.
  • Dynamic model weighting based on instance characteristics optimizes performance and computational feasibility.
  • The method demonstrates superior performance compared to existing approaches across diverse QA scenarios.