神经机器没有排名
IEEE transactions on neural networks and learning systems
|December 23, 2025
概括
我们在神经信息检索 (IR) 中为隐私引入神经机器排名 (NuMuR). 我们的CoCoL方法有效地删除数据,同时保持模型性能,解决选择性删除信息的关键挑战.
科学领域:
- 信息检索 信息检索
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 对数据隐私遵守和神经IR系统中选择性删除信息的需求日益增加.
- 现有的机器取消学习方法对于神经IR来说是不理想的,原因是不规范的分数和纠的数据场景.
- 神经排名器输出非正常的相关性得分,挑战传统的蒸框架.
研究的目的:
- 介绍神经机器排名 (NuMuR) 作为神经IR中机器排名的新任务.
- 解决现有的去学习方法在处理神经排名和纠数据方面的局限性.
- 提出一个新的框架,对比和一致的损失 (CoCoL),以有效和可控的数据删除.
主要方法:
- 开发了一个双重目标的框架,对比和一致的损失 (CoCoL).
- CoCoL 结合了对比损失,以减少忘记设置得分,并保持纠样本的性能.
- 一个一致的损失组件可以保持保留集的准确性.
主要成果:
- CoCoL实现了对指定的数据的大量遗忘.
- 观察到保留和概括表现的最小损失.
- 在两个数据集和四个神经IR模型中证明了有效性.
结论:
- CoCoL提供了一种更有效和可控的方法来删除神经IR系统中的数据.
- 拟议的框架成功地解决了在这个领域机器失学的独特挑战.
- NuMuR促进了神经IR中增强的数据隐私和选择性信息管理.
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