基于LLM的推系统的逻辑级平衡机器取消学习
IEEE transactions on neural networks and learning systems
|February 10, 2026
概括
本研究介绍了基于大型语言模型 (LLMRec) 推系统的新型失学系统,以解决数据治理问题. 适配器驱动的逻辑修改方法有效地删除数据,同时保持推性能和核心语言功能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 推系统对于数字平台至关重要,大型语言模型 (LLM) 提高了个性化和准确性.
- 基于LLM的推 (LLMRec) 系统面临数据治理挑战,包括隐私,过时,有毒和受版权保护的数据,需要有效删除数据.
- 现有的方法难以准确地从复杂的LLMRec系统中删除数据及其影响.
研究的目的:
- 提出和评估一个LLMRec去学习系统,能够精确地删除数据,同时保持系统性能.
- 通过一种新的失学机制,解决LLMRec系统中数据治理的挑战.
主要方法:
- 开发了一个适配器驱动的逻辑修改系统,用于LLMRec的学习.
- 使用适配器在失学过程中降低培训成本.
- 使用知识蒸 (KD) 驱动的逻辑修改,以确保继续有效的推理和推质量.
- 整合了一个基于适配器的一般知识保留模块,以保留核心LLM功能.
主要成果:
- 拟议的系统在LLMRec系统中展示了有效和高效的学习.
- 适配器驱动的方法成功地删除了目标数据,同时保持了建议的准确性.
- 知识蒸和一般知识保留模块保持模型推理和基础语言能力.
结论:
- 适配器驱动的逻辑修改系统为LLMRec系统中的失学提供了精确有效的解决方案.
- 该方法平衡了数据删除要求与保持推性能和一般语言理解的需求.
- 这项工作在解决基于LLM的推框架内的数据治理挑战方面取得了重大进展.
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