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Updated: Jan 12, 2026

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

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

Published on: December 6, 2024

基于meta-learner的框架用于可解释的电子邮件垃圾邮件检测.

Meghana Kshirsagar^1,2, Vedant Rathi³, Conor Ryan^1,2

¹Biocomputing Developmental Systems Research Group, Department of Computer Science and Information Systems, University of Limerick, Limerick, Ireland.

Frontiers in artificial intelligence

|November 6, 2025

概括

此摘要是机器生成的。

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使用条款与条件

一个新的meta-learner显著改善了垃圾邮件电子邮件的分类. 这种先进的模型优于传统的机器学习和深度学习方法,为现实应用提供更强大,更有效的垃圾邮件过.

科学领域:

计算机科学计算机科学
人工智能的人工智能
机器学习机器学习

背景情况:

电子邮件仍然是一个关键的通信道,但受到普遍的垃圾邮件的挑战.
有效的垃圾邮件分类对于高效的数字通信和用户体验至关重要.

研究的目的:

开发和评估用于垃圾邮件电子邮件分类的新型元学习器.
将元学习者的表现与传统的机器学习 (ML) 和深度学习 (DL) 模型进行比较.
评估各种词嵌入,矢量化方案和模型架构对垃圾邮件检测的影响.

主要方法:

开发了一个新的meta-learner,并与五个ML和五个DL垃圾邮件分类器进行了比较.
对Enron-Spam和TREC 2007数据集进行了性能评估,包括混合数据集.
超学习者的预测与个别模型预测相对应.

主要成果:

在混合数据集上,meta-learner实现了卓越的性能,准确度为0.9905,AUC为0.9991.
它的性能优于现有的最先进的模型,包括唯一的其他元学习垃圾邮件检测模型.
在对未见数据的零射击设置中,它实现了0.8970的垃圾邮件敏感度和0.7605的AUC.

关键词:

算法偏差是一种算法偏差.这是分类分类的分类.数据偏差是一种数据偏差.深度学习是一种深度学习.机器学习是机器学习.这是一个meta-learner学习者.自然语言处理自然语言处理.垃圾邮件电子邮件检测垃圾邮件检测

相关实验视频

Last Updated: Jan 12, 2026

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

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

Published on: December 6, 2024

结论:

超级学习为垃圾邮件过提供了一个强大的,抗偏见的方法,适合于现实世界的部署.
结合多样化的模型优势,增强了对不断变化的垃圾邮件策略的弹性.
拟议的meta-learner提供了更好的准确性,概括性和计算效率.