# Knowledge Sanitization of Large Language Models

arXiv: https://arxiv.org/abs/2309.11852 · HTML: https://mlsanity.com/paper/2309.11852/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 38 references that are in the map.
- First posted on arXiv 21 Sep 2023.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019)
- [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018)
- [Transformer Feed-Forward Layers Are Key-Value Memories](https://mlsanity.com/paper/2012.14913/) (2020)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [Editing Models with Task Arithmetic](https://mlsanity.com/paper/2212.04089/) (2022)
- [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://mlsanity.com/paper/1704.04683/) (2017)
- [Quantifying Memorization Across Neural Language Models](https://mlsanity.com/paper/2202.07646/) (2022)
- [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://mlsanity.com/paper/2204.06745/) (2022)
- [Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks](https://mlsanity.com/paper/1911.04933/) (2019)
- [Knowledge Neurons in Pretrained Transformers](https://mlsanity.com/paper/2104.08696/) (2021)
- [Knowledge Unlearning for Mitigating Privacy Risks in Language Models](https://mlsanity.com/paper/2210.01504/) (2022)
- [ToTTo: A Controlled Table-To-Text Generation Dataset](https://mlsanity.com/paper/2004.14373/) (2020)
- [Are Large Pre-Trained Language Models Leaking Your Personal Information?](https://mlsanity.com/paper/2205.12628/) (2022)
- [Formalizing Data Deletion in the Context of the Right to be Forgotten](https://mlsanity.com/paper/2002.10635/) (2020)
- [C-sanitized: a privacy model for document redaction and sanitization](https://mlsanity.com/paper/1406.4285/) (2014)

## Built on by (most cited first, 30 of 37)

- [Rethinking Machine Unlearning for Large Language Models](https://mlsanity.com/paper/2402.08787/) (2024)
- [Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs](https://mlsanity.com/paper/2407.15549/) (2024)
- [Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning](https://mlsanity.com/paper/2410.07163/) (2024)
- [A Comprehensive Study of Knowledge Editing for Large Language Models](https://mlsanity.com/paper/2401.01286/) (2024)
- [Eight Methods to Evaluate Robust Unlearning in LLMs](https://mlsanity.com/paper/2402.16835/) (2024)
- [RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models](https://mlsanity.com/paper/2406.10890/) (2024)
- [A Closer Look at Machine Unlearning for Large Language Models](https://mlsanity.com/paper/2410.08109/) (2024)
- [LLM Unlearning via Neural Activation Redirection](https://mlsanity.com/paper/2502.07218/) (2025)
- [Identifying and Mitigating Privacy Risks Stemming from Language Models: A Survey](https://mlsanity.com/paper/2310.01424/) (2023)
- [Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey](https://mlsanity.com/paper/2406.05392/) (2024)
- [UnStar: Unlearning with Self-Taught Anti-Sample Reasoning for LLMs](https://mlsanity.com/paper/2410.17050/) (2024)
- [Lifelong Knowledge Editing for Vision Language Models with Low-Rank Mixture-of-Experts](https://mlsanity.com/paper/2411.15432/) (2024)
- [SoK: Machine Unlearning for Large Language Models](https://mlsanity.com/paper/2506.09227/) (2025)
- [Unlearning That Lasts: Utility-Preserving, Robust, and Almost Irreversible Forgetting in LLMs](https://mlsanity.com/paper/2509.02820/) (2025)
- [UCD: Unlearning in LLMs via Contrastive Decoding](https://mlsanity.com/paper/2506.12097/) (2025)
- [SoK: Prompt Hacking of Large Language Models](https://mlsanity.com/paper/2410.13901/) (2024)
- [CLUE: Conflict-guided Localization for LLM Unlearning Framework](https://mlsanity.com/paper/2509.20977/) (2025)
- [Attention Smoothing Is All You Need For Unlearning](https://mlsanity.com/paper/2603.01285/) (2026)
- [Recent Advances in Attack and Defense Approaches of Large Language Models](https://mlsanity.com/paper/2409.03274/) (2024)
- [Align-then-Unlearn: Embedding Alignment for LLM Unlearning](https://mlsanity.com/paper/2506.13181/) (2025)
- [Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning](https://mlsanity.com/paper/2510.04773/) (2025)
- [RESTOR: Knowledge Recovery in Machine Unlearning](https://mlsanity.com/paper/2411.00204/) (2024)
- [The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare](https://mlsanity.com/paper/2411.03287/) (2024)
- [Helping Large Language Models Protect Themselves: An Enhanced Filtering and Summarization System](https://mlsanity.com/paper/2505.01315/) (2025)
- [Towards Evaluation for Real-World LLM Unlearning](https://mlsanity.com/paper/2508.01324/) (2025)
- [Towards Safer Chatbots: Automated Policy Compliance Evaluation of Custom GPTs](https://mlsanity.com/paper/2502.01436/) (2025)
- [MIAT: Maneuver-Intention-Aware Transformer for Spatio-Temporal Trajectory Prediction](https://mlsanity.com/paper/2504.05059/) (2025)
- [Layered Unlearning for Adversarial Relearning](https://mlsanity.com/paper/2505.09500/) (2025)
- [Learning-Time Encoding Shapes Unlearning in LLMs](https://mlsanity.com/paper/2506.15076/) (2025)
- [Forgetting-MarI: LLM Unlearning via Marginal Information Regularization](https://mlsanity.com/paper/2511.11914/) (2025)

## Abstract (arXiv)

We explore a knowledge sanitization approach to mitigate the privacy concerns associated with large language models (LLMs). LLMs trained on a large corpus of Web data can memorize and potentially reveal sensitive or confidential information, raising critical security concerns. Our technique efficiently fine-tunes these models using the Low-Rank Adaptation (LoRA) method, prompting them to generate harmless responses such as ``I don't know'' when queried about specific information. Experimental results in a closed-book question-answering task show that our straightforward method not only minimizes particular knowledge leakage but also preserves the overall performance of LLMs. These two advantages strengthen the defense against extraction attacks and reduces the emission of harmful content such as hallucinations.