# Mechanistic Unlearning: Robust Knowledge Unlearning and Editing via Mechanistic Localization

arXiv: https://arxiv.org/abs/2410.12949 · HTML: https://mlsanity.com/paper/2410.12949/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Placed in Machine unlearning, one of 12 directions in LLM safety and security.
- Builds on 35 of its 53 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Placed in

- LLM safety and security / [Machine unlearning](https://mlsanity.com/safety/machine-unlearning/)

## Builds on (most cited first, 30 of 35)

- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal](https://mlsanity.com/paper/2402.04249/) (2024)
- [Understanding intermediate layers using linear classifier probes](https://mlsanity.com/paper/1610.01644/) (2016)
- [Representation Engineering: A Top-Down Approach to AI Transparency](https://mlsanity.com/paper/2310.01405/) (2023)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Dissecting Recall of Factual Associations in Auto-Regressive Language Models](https://mlsanity.com/paper/2304.14767/) (2023)
- [The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning](https://mlsanity.com/paper/2403.03218/) (2024)
- [Making AI Forget You: Data Deletion in Machine Learning](https://mlsanity.com/paper/1907.05012/) (2019)
- [Who's Harry Potter? Approximate Unlearning in LLMs](https://mlsanity.com/paper/2310.02238/) (2023)
- [Improving Alignment and Robustness with Circuit Breakers](https://mlsanity.com/paper/2406.04313/) (2024)
- [tinyBenchmarks: evaluating LLMs with fewer examples](https://mlsanity.com/paper/2402.14992/) (2024)
- [Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models](https://mlsanity.com/paper/2301.04213/) (2023)
- [Unlearn What You Want to Forget: Efficient Unlearning for LLMs](https://mlsanity.com/paper/2310.20150/) (2023)
- [Surgical Fine-Tuning Improves Adaptation to Distribution Shifts](https://mlsanity.com/paper/2210.11466/) (2022)
- [Localizing Model Behavior with Path Patching](https://mlsanity.com/paper/2304.05969/) (2023)
- [A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity](https://mlsanity.com/paper/2401.01967/) (2024)
- [Eight Methods to Evaluate Robust Unlearning in LLMs](https://mlsanity.com/paper/2402.16835/) (2024)
- [Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks](https://mlsanity.com/paper/2309.17410/) (2023)
- [Tamper-Resistant Safeguards for Open-Weight LLMs](https://mlsanity.com/paper/2408.00761/) (2024)
- [Task-Specific Skill Localization in Fine-tuned Language Models](https://mlsanity.com/paper/2302.06600/) (2023)
- [Do Unlearning Methods Remove Information from Language Model Weights?](https://mlsanity.com/paper/2410.08827/) (2024)
- [Eliciting Latent Knowledge from Quirky Language Models](https://mlsanity.com/paper/2312.01037/) (2023)
- [Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models](https://mlsanity.com/paper/2211.14946/) (2022)
- [Characterizing Mechanisms for Factual Recall in Language Models](https://mlsanity.com/paper/2310.15910/) (2023)
- [Summing Up the Facts: Additive Mechanisms Behind Factual Recall in LLMs](https://mlsanity.com/paper/2402.07321/) (2024)
- [Intrinsic Test of Unlearning Using Parametric Knowledge Traces](https://mlsanity.com/paper/2406.11614/) (2024)

## Built on by (most cited first, 30 of 35)

- [SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders](https://mlsanity.com/paper/2501.18052/) (2025)
- [Steering Out-of-Distribution Generalization with Concept Ablation Fine-Tuning](https://mlsanity.com/paper/2507.16795/) (2025)
- [A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models](https://mlsanity.com/paper/2503.01854/) (2025)
- [Editing as Unlearning: Are Knowledge Editing Methods Strong Baselines for Large Language Model Unlearning?](https://mlsanity.com/paper/2505.19855/) (2025)
- [SoK: Machine Unlearning for Large Language Models](https://mlsanity.com/paper/2506.09227/) (2025)
- [CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners](https://mlsanity.com/paper/2503.16356/) (2025)
- [Attention Smoothing Is All You Need For Unlearning](https://mlsanity.com/paper/2603.01285/) (2026)
- [Step-by-Step Reasoning Attack: Revealing 'Erased' Knowledge in Large Language Models](https://mlsanity.com/paper/2506.17279/) (2025)
- [Is Gradient Ascent Really Necessary? Memorize to Forget for Machine Unlearning](https://mlsanity.com/paper/2602.06441/) (2026)
- [Erasing Without Remembering: Implicit Knowledge Forgetting in Large Language Models](https://mlsanity.com/paper/2502.19982/) (2025)
- [DUSK: Do Not Unlearn Shared Knowledge](https://mlsanity.com/paper/2505.15209/) (2025)
- [On the creation of narrow AI: hierarchy and nonlocality of neural network skills](https://mlsanity.com/paper/2505.15811/) (2025)
- [A Survey on Unlearning in Large Language Models](https://mlsanity.com/paper/2510.25117/) (2025)
- [AlignSAE: Concept-Aligned Sparse Autoencoders](https://mlsanity.com/paper/2512.02004/) (2025)
- [Models Designed to Forget: Machine Unlearning via Key Deletion](https://mlsanity.com/paper/2603.15033/) (2026)
- [LLM Unlearning using Gradient Ratio-Based Influence Estimation and Noise Injection](https://mlsanity.com/paper/2508.06467/) (2025)
- [MIAT: Maneuver-Intention-Aware Transformer for Spatio-Temporal Trajectory Prediction](https://mlsanity.com/paper/2504.05059/) (2025)
- [Learning-Time Encoding Shapes Unlearning in LLMs](https://mlsanity.com/paper/2506.15076/) (2025)
- [RippleBench: Capturing Ripple Effects Using Existing Knowledge Repositories](https://mlsanity.com/paper/2512.04144/) (2025)
- [Detoxifying LLMs via Representation Erasure-Based Preference Optimization](https://mlsanity.com/paper/2602.23391/) (2026)
- [Forgetting That Sticks: Quantization-Permanent Unlearning via Circuit Attribution](https://mlsanity.com/paper/2605.15138/) (2026)
- [Measuring the Depth of LLM Unlearning via Activation Patching](https://mlsanity.com/paper/2605.24614/) (2026)
- [Toward a Benchmark for Controllable Simulation of Imperfect Students with Large Language Models](https://mlsanity.com/paper/2605.25601/) (2026)
- [Exposing the Illusion of Erasure in Knowledge Editing for LLMs](https://mlsanity.com/paper/2606.23276/) (2026)
- [Measure, Don't Optimize: Forecasting Recovery in LLM Unlearning](https://mlsanity.com/paper/2608.11408/) (2026)
- [Variance-Reduced (varepsilon,δ)-Unlearning using Forget Set Gradients](https://mlsanity.com/paper/2602.14938/) (2026)
- [U-CECE: A Universal Multi-Resolution Framework for Conceptual Counterfactual Explanations](https://mlsanity.com/paper/2604.08295/) (2026)
- [MAAT: Multi-phase Adapter-Aware Targeted Unlearning](https://mlsanity.com/paper/2605.30514/) (2026)
- [Beyond Static Interpretability: Anticipating Post-SFT Mechanisms from Pre-SFT Parameters for Better Tuning](https://mlsanity.com/paper/2608.24482/) (2026)
- [Distance Is Not Enough: Forget-Retain Alignment Gap Predicts LLM Relearning Robustness](https://mlsanity.com/paper/2608.25429/) (2026)

## Abstract (arXiv)

Methods for knowledge editing and unlearning in large language models seek to edit or remove undesirable knowledge or capabilities without compromising general language modeling performance. This work investigates how mechanistic interpretability -- which, in part, aims to identify model components (circuits) associated to specific interpretable mechanisms that make up a model capability -- can improve the precision and effectiveness of editing and unlearning. We find a stark difference in unlearning and edit robustness when training components localized by different methods. We highlight an important distinction between methods that localize components based primarily on preserving outputs, and those finding high level mechanisms with predictable intermediate states. In particular, localizing edits/unlearning to components associated with the lookup-table mechanism for factual recall 1) leads to more robust edits/unlearning across different input/output formats, and 2) resists attempts to relearn the unwanted information, while also reducing unintended side effects compared to baselines, on both a sports facts dataset and the CounterFact dataset across multiple models. We also find that certain localized edits disrupt the latent knowledge in the model more than any other baselines, making unlearning more robust to various attacks.