# Precise Localization of Memories: A Fine-grained Neuron-level Knowledge Editing Technique for LLMs

arXiv: https://arxiv.org/abs/2503.01090 · HTML: https://mlsanity.com/paper/2503.01090/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2025.

## Built on by (most cited first, 18 of 18)

- [Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning](https://mlsanity.com/paper/2601.03190/) (2026)
- [UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Language Models](https://mlsanity.com/paper/2505.14679/) (2025)
- [BalDRO: A Distributionally Robust Optimization based Framework for Large Language Model Unlearning](https://mlsanity.com/paper/2601.09172/) (2026)
- [Edit Less, Achieve More: Dynamic Sparse Neuron Masking for Lifelong Knowledge Editing in LLMs](https://mlsanity.com/paper/2510.22139/) (2025)
- [Where Knowledge Collides: A Mechanistic Study of Intra-Memory Knowledge Conflict in Language Models](https://mlsanity.com/paper/2601.09445/) (2026)
- [CrispEdit: Low-Curvature Projections for Scalable Non-Destructive LLM Editing](https://mlsanity.com/paper/2602.15823/) (2026)
- [EvoEdit: Lifelong Free-Text Knowledge Editing through Latent Perturbation Augmentation and Knowledge-driven Parameter Fusion](https://mlsanity.com/paper/2512.04545/) (2025)
- [Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablation](https://mlsanity.com/paper/2605.03058/) (2026)
- [Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models](https://mlsanity.com/paper/2506.08427/) (2025)
- [Bilinear representation mitigates reversal curse and enables consistent model editing](https://mlsanity.com/paper/2509.21993/) (2025)
- [Correct When Paired, Wrong When Split: Decoupling and Editing Modality-Specific Neurons in MLLMs](https://mlsanity.com/paper/2606.17057/) (2026)
- [Targeted Recovery of Weight-Space Mechanisms From Neural Networks](https://mlsanity.com/paper/2607.13047/) (2026)
- [ForgetBench: Benchmarking Forgetting Dynamics of Long-Term Parametric Memory in Language Models](https://mlsanity.com/paper/2607.26455/) (2026)
- [ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving Systems](https://mlsanity.com/paper/2503.20756/) (2025)
- [Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment](https://mlsanity.com/paper/2605.23780/) (2026)
- [KnowledgeDebugger -- an Exploration Tool for Knowledge Localization and Editing in Transformers](https://mlsanity.com/paper/2607.01000/) (2026)
- [A Tropical Geometry View of Forgetting: A Per-Unit Projector for Knowledge-Preserving Fine-Tuning](https://mlsanity.com/paper/2610.04670/) (2026)
- [Mind the Gaps: From Failure Attribution to Closed-Form Repair of Code Language Models](https://mlsanity.com/paper/2610.05277/) (2026)

## Abstract (arXiv)

Knowledge editing aims to update outdated information in Large Language Models (LLMs). A representative line of study is locate-then-edit methods, which typically employ causal tracing to identify the modules responsible for recalling factual knowledge about entities. However, we find these methods are often sensitive only to changes in the subject entity, leaving them less effective at adapting to changes in relations. This limitation results in poor editing locality, which can lead to the persistence of irrelevant or inaccurate facts, ultimately compromising the reliability of LLMs. We believe this issue arises from the insufficient precision of knowledge localization. To address this, we propose a Fine-grained Neuron-level Knowledge Editing (FiNE) method that enhances editing locality without affecting overall success rates. By precisely identifying and modifying specific neurons within feed-forward networks, FiNE significantly improves knowledge localization and editing. Quantitative experiments demonstrate that FiNE efficiently achieves better overall performance compared to existing techniques, providing new insights into the localization and modification of knowledge within LLMs.