title: Learning to Refuse: Towards Mitigating Privacy Risks in LLMs
summary: Large language models (LLMs) exhibit remarkable capabilities in understanding
  and generating natural language. However, these models can inadvertently
  memorize private information, posing significant privacy risks. This study
  addresses the challenge of enabling LLMs to protect specific individuals'
  private data without the need for complete retraining. We propose \return, a
  Real-world pErsonal daTa UnleaRNing dataset, comprising 2,492 individuals from
  Wikipedia with associated QA pairs, to evaluate machine unlearning (MU) methods
  for protecting personal data in a realistic scenario. Additionally, we
  introduce the Name-Aware Unlearning Framework (NAUF) for Privacy Protection,
  which enables the model to learn which individuals' information should be
  protected without affecting its ability to answer questions related to other
  unrelated individuals. Our extensive experiments demonstrate that NAUF achieves
  a state-of-the-art average unlearning score, surpassing the best baseline
  method by 5.65 points, effectively protecting target individuals' personal data
  while maintaining the model's general capabilities.
paper_authors._id: 66960be0659a52fa2757ae13
paper_authors.name: Zhenhua Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-18T09:07:57.365Z
paper_authors._id: 619b00dd4b0db5ca9d3ea35f
paper_authors.avatarUrl: /avatars/fce5ac388b7f10cbbc63e9992a5a799f.svg
paper_authors.fullname: Zhenhua Liu
paper_authors.type: user
paper_authors.user: zhliu
paper_authors._id: 66960be0659a52fa2757ae14
paper_authors.name: Tong Zhu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-16T20:15:23.759Z
paper_authors._id: 629454301ae2138079f7ff31
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/629454301ae2138079f7ff31/rVtbF-j06gDiYzomTeVTc.jpeg
paper_authors.fullname: Tong Zhu
paper_authors.type: user
paper_authors.user: Spico
paper_authors._id: 66960be0659a52fa2757ae15
paper_authors.name: Chuanyuan Tan
paper_authors._id: 66960be0659a52fa2757ae16
paper_authors.name: Wenliang Chen
paper_title: Learning to Refuse: Towards Mitigating Privacy Risks in LLMs
paper_summary: Large language models (LLMs) exhibit remarkable capabilities in understanding
  and generating natural language. However, these models can inadvertently
  memorize private information, posing significant privacy risks. This study
  addresses the challenge of enabling LLMs to protect specific individuals'
  private data without the need for complete retraining. We propose \return, a
  Real-world pErsonal daTa UnleaRNing dataset, comprising 2,492 individuals from
  Wikipedia with associated QA pairs, to evaluate machine unlearning (MU) methods
  for protecting personal data in a realistic scenario. Additionally, we
  introduce the Name-Aware Unlearning Framework (NAUF) for Privacy Protection,
  which enables the model to learn which individuals' information should be
  protected without affecting its ability to answer questions related to other
  unrelated individuals. Our extensive experiments demonstrate that NAUF achieves
  a state-of-the-art average unlearning score, surpassing the best baseline
  method by 5.65 points, effectively protecting target individuals' personal data
  while maintaining the model's general capabilities.
paper_ai_summary: The Name-Aware Unlearning Framework for Privacy Protection demonstrates superior capabilities in protecting individual private data in large language models without needing complete retraining.
paper_ai_keywords: LLMs large language models machine unlearning Real-world pErsonal daTa UnleaRNing Name-Aware Unlearning Framework (NAUF)