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English(EN) Learning to Erase Private Knowledge from Multi-Documents for Retrieval-Augmented Large Language Models

新的RAG方法Eraser4RAG移除私有数据，性能超越GPT-4o

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了Eraser4RAG，一种用于从检索增强生成（RAG）系统中使用的文档中移除敏感信息的新颖方法。该方法构建知识图谱以识别和区分私有信息与公共信息，然后微调模型重写文档，排除私有三元组，同时保留公共知识。实验表明，Eraser4RAG在有效擦除私有数据同时保持公共信息对生成任务的效用方面优于GPT-4o。 AI

影响 通过实现敏感数据的自定义擦除而不损害生成能力，增强了RAG系统的隐私性。

排序理由 该集群包含一篇详细介绍RAG系统隐私新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的RAG方法Eraser4RAG移除私有数据，性能超越GPT-4o

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该集群包含一篇详细介绍RAG系统隐私新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yujing Wang, Jinwen Chen, Hainan Zhang, Liang Pang, Yongxin Tong, Binghui Guo, Hongwei Zheng, Zhiming Zheng ·

    从多文档中学习擦除私有知识以用于检索增强型大型语言模型

    arXiv:2504.09910v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) is a promising technique for applying LLMs to proprietary domains. However, retrieved documents may contain sensitive knowledge, posing risks of privacy leakage in generative results. Thus, e…

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