![[2602.20207] Golden Layers and Where to Find Them: Improved Knowledge Editing for Large Language Models Via Layer Gradient Analysis](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.20207] Golden Layers and Where to Find Them: Improved Knowledge Editing for Large Language Models Via Layer Gradient Analysis
Summary
This article discusses the concept of 'golden layers' in large language models (LLMs) and presents a novel method, Layer Gradient Analysis (LGA), to efficiently identify these layers for improved knowledge editing.
Why It Matters
Understanding how to effectively edit knowledge in LLMs is crucial for enhancing their performance and reliability. The proposed method could streamline the editing process, making it more efficient and accessible for researchers and developers in AI.
Key Takeaways
- Golden layers can achieve near-optimal knowledge editing performance in LLMs.
- Layer Gradient Analysis (LGA) provides an efficient way to identify these layers.
- The method generalizes well across different datasets and LLM types.
Computer Science > Machine Learning arXiv:2602.20207 (cs) [Submitted on 22 Feb 2026] Title:Golden Layers and Where to Find Them: Improved Knowledge Editing for Large Language Models Via Layer Gradient Analysis Authors:Shrestha Datta, Hongfu Liu, Anshuman Chhabra View a PDF of the paper titled Golden Layers and Where to Find Them: Improved Knowledge Editing for Large Language Models Via Layer Gradient Analysis, by Shrestha Datta and 2 other authors View PDF HTML (experimental) Abstract:Knowledge editing in Large Language Models (LLMs) aims to update the model's prediction for a specific query to a desired target while preserving its behavior on all other inputs. This process typically involves two stages: identifying the layer to edit and performing the parameter update. Intuitively, different queries may localize knowledge at different depths of the model, resulting in different sample-wise editing performance for a fixed editing layer. In this work, we hypothesize the existence of fixed golden layers that can achieve near-optimal editing performance similar to sample-wise optimal layers. To validate this hypothesis, we provide empirical evidence by comparing golden layers against ground-truth sample-wise optimal layers. Furthermore, we show that golden layers can be reliably identified using a proxy dataset and generalize effectively to unseen test set queries across datasets. Finally, we propose a novel method, namely Layer Gradient Analysis (LGA) that estimates golden l...