| Challenge: | Large language models (LLMs) exhibit remarkable capabilities in understanding and generating natural languages, but can inadvertently memorize private information, posing significant privacy risks. |
| Approach: | They propose to use a dataset to evaluate machine unlearning methods for protecting personal data in a realistic scenario. |
| Outcome: | The proposed model outperforms baseline methods by 5.65 points and protects target individuals’ personal data while maintaining general capabilities. |
Similar Papers
Knowledge Unlearning for Mitigating Privacy Risks in Language Models (2023.acl-long)
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| Challenge: | Recent work shows that an adversary can extract training data from Pretrained Language Models including Personally Identifiable Information (PII) such as names, phone numbers, and email addresses. |
| Approach: | They propose to use knowledge unlearning to reduce privacy risks for LMs by performing gradient ascent on target token sequences instead of trying to unlearn all the data at once. |
| Outcome: | The proposed method can give a stronger empirical privacy guarantee in scenarios where the data vulnerable to extraction attacks are known a priori while being much more efficient and robust. |
Preserving Privacy Through Dememorization: An Unlearning Technique For Mitigating Memorization Risks In Language Models (2023.emnlp-main)
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| Challenge: | Large Language models (LLMs) are trained on vast amounts of data, including sensitive information that poses a risk to personal privacy if exposed. |
| Approach: | They propose a novel unlearning approach that utilizes an efficient reinforcement learning feedback loop via proximal policy optimization to incentivize the LLMs to learn a paraphrasing policy to unlearn the pre-training data. |
| Outcome: | The proposed approach surpasses strong baselines and state-of-the-art methods in terms of its ability to generalize and strike a balance between privacy and LLM performance. |
Unlearn What You Want to Forget: Efficient Unlearning for LLMs (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) can be used to memorize a vast amount of data, but can suffer from privacy issues and data protection violations. |
| Approach: | They propose an efficient unlearning framework that could update LLMs without retraining them . they introduce lightweight unlearning layers learned with a selective teacher-student objective into transformers . |
| Outcome: | The proposed framework could update LLMs without having to retrain the whole model after data removals. |
Towards Robust Evaluation of Unlearning in LLMs via Data Transformations (2024.findings-emnlp)
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Abhinav Joshi, Shaswati Saha, Divyaksh Shukla, Sriram Vema, Harsh Jhamtani, Manas Gaur, Ashutosh Modi
| Challenge: | Large Language Models (LLMs) have shown to be a great success in a wide range of applications ranging from regular NLP-based use cases to AI agents. |
| Approach: | They examine the robustness of existing MUL techniques for their ability to enable leakage-proof forgetting in LLMs. |
| Outcome: | The proposed methods can be used to enable leakage-proof forgetting in LLMs. |
Controlling What You Share: Assessing Language Model Adherence to Privacy Preferences (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are accessed via commercial APIs, but expose data to service providers. |
| Approach: | They propose a framework where a local model uses natural language instructions to rewrite queries and paired them with synthetic privacy profiles to achieve better privacy preservation. |
| Outcome: | The proposed model outperforms large-scale few-shot models in terms of privacy preservation and performance. |
Machine Unlearning of Pre-trained Large Language Models (2024.acl-long)
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| Challenge: | Using curated datasets, we establish a robust benchmark for unlearning performance, demonstrating that these methods are over 105 times more computationally efficient than retraining. |
| Approach: | They propose a framework for machine unlearning in pre-trained LLMs and integrate gradient ascent with gradient descent on in-distribution data to achieve robustness. |
| Outcome: | The proposed framework is over 105 times more efficient than retraining on in-distribution data and provides detailed guidelines for efficient hyperparameter tuning in the unlearning process. |
Combating Security and Privacy Issues in the Era of Large Language Models (2024.naacl-tutorials)
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| Challenge: | a tutorial aims to provide a summary of risks and vulnerabilities in large language models . a number of studies have focused on security, privacy and copyright aspects of LLMs . |
| Approach: | This tutorial seeks to provide a systematic summary of risks and vulnerabilities in large language models . authors will discuss security, privacy and copyright aspects of LLMs . |
| Outcome: | This tutorial aims to provide a systematic summary of risks and vulnerabilities in large language models . it will also outline emerging challenges in security, privacy and reliability of LLMs . |
Large Language Models Can Be Contextual Privacy Protection Learners (2024.emnlp-main)
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Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable linguistic comprehension and generation capability, but when applied to specialized industries, they face challenges such as hallucination, insufficient domain knowledge, and failing to incorporate the latest domain knowledge. |
| Approach: | They propose a paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy. |
| Outcome: | The proposed model protects private data while enhancing the model's knowledge. |
Reveal and Release: Iterative LLM Unlearning with Self-generated Data (2025.findings-emnlp)
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| Challenge: | Existing approaches to unlearning large language models assume full access to the forget dataset, overlooking two key challenges: (1) Forget data is often privacy-sensitive, rare, or legally regulated, making it expensive or impractical to obtain (2) The distribution of available forget data may not align with how that information is represented within the model. |
| Approach: | They propose a “Reveal-and-Release” method to unlearn with self-generated data, prompting the model to reveal what it knows using optimized instructions. |
| Outcome: | The proposed method removes the influence of undesirable data from the model. |
R.R.: Unveiling LLM Training Privacy through Recollection and Ranking (2025.findings-acl)
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| Challenge: | Existing privacy attacks focus on membership inference or data extraction, but reconstructing specific personally identifiable information (PII) in training data remains challenging. |
| Approach: | They propose a two-step privacy stealing attack that enables attackers to reconstruct PII entities from scrubbed training data where the PI I entities have been masked. |
| Outcome: | The proposed attack can reconstruct PII entities from scrubbed training data where the PI I entities have been masked. |