Is Bigger Edit Batch Size Always Better? -- An Empirical Study on Model Editing with Llama-3
- URL: http://arxiv.org/abs/2405.00664v1
- Date: Wed, 1 May 2024 17:50:37 GMT
- Title: Is Bigger Edit Batch Size Always Better? -- An Empirical Study on Model Editing with Llama-3
- Authors: Junsang Yoon, Akshat Gupta, Gopala Anumanchipalli,
- Abstract summary: This study presents a targeted model editing analysis focused on the latest large language model, Llama-3.
We identify the most effective layers for targeted edits through an evaluation that encompasses up to 4096 edits.
- Score: 2.569159339315845
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This study presents a targeted model editing analysis focused on the latest large language model, Llama-3. We explore the efficacy of popular model editing techniques - ROME, MEMIT, and EMMET, which are designed for precise layer interventions. We identify the most effective layers for targeted edits through an evaluation that encompasses up to 4096 edits across three distinct strategies: sequential editing, batch editing, and a hybrid approach we call as sequential-batch editing. Our findings indicate that increasing edit batch-sizes may degrade model performance more significantly than using smaller edit batches sequentially for equal number of edits. With this, we argue that sequential model editing is an important component for scaling model editing methods and future research should focus on methods that combine both batched and sequential editing. This observation suggests a potential limitation in current model editing methods which push towards bigger edit batch sizes, and we hope it paves way for future investigations into optimizing batch sizes and model editing performance.
Related papers
- Consecutive Model Editing with Batch alongside HooK Layers [59.673084839708224]
COMEBA-HK is a model editing method that is both consecutive and batch-supportive.
It is memory-friendly as it only needs a small amount of it to store several hook layers with updated weights.
arXiv Detail & Related papers (2024-03-08T14:07:44Z) - The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse [58.0132400208411]
Even a single edit can trigger model collapse, manifesting as significant performance degradation in various benchmark tasks.
benchmarking Large Language Models after each edit is impractically time-consuming and resource-intensive.
We have utilized GPT-3.5 to develop a new dataset, HardEdit, based on hard cases.
arXiv Detail & Related papers (2024-02-15T01:50:38Z) - Model Editing at Scale leads to Gradual and Catastrophic Forgetting [2.569159339315845]
We evaluate the current model editing methods at scale, focusing on two state of the art methods: ROME and MEMIT.
We find that as the model is edited sequentially with multiple facts, it continually forgets previously edited facts and the ability to perform downstream tasks.
arXiv Detail & Related papers (2024-01-15T03:57:15Z) - Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue [122.20016030723043]
Model editing is a technique that edits large language models (LLMs) with updated knowledge to alleviate hallucinations without resource-intensive retraining.
Current model editing methods can effectively modify a model's behavior within a specific area of interest.
They often overlook the potential unintended side effects on the general abilities of LLMs.
arXiv Detail & Related papers (2024-01-09T18:03:15Z) - Object-aware Inversion and Reassembly for Image Editing [61.19822563737121]
We propose Object-aware Inversion and Reassembly (OIR) to enable object-level fine-grained editing.
We use our search metric to find the optimal inversion step for each editing pair when editing an image.
Our method achieves superior performance in editing object shapes, colors, materials, categories, etc., especially in multi-object editing scenarios.
arXiv Detail & Related papers (2023-10-18T17:59:02Z) - Edit at your own risk: evaluating the robustness of edited models to
distribution shifts [0.0]
We investigate how model editing affects the general robustness of a model, as well as the robustness of the specific behavior targeted by the edit.
We find that edits tend to reduce general robustness, but that the degree of degradation depends on the editing algorithm and layers chosen.
Motivated by these observations we introduce a new model editing algorithm, 1-layer (1-LI), which uses weight-space to navigate the trade-off between editing task accuracy and general robustness.
arXiv Detail & Related papers (2023-02-28T19:41:37Z) - Memory-Based Model Editing at Scale [102.28475739907498]
Existing model editors struggle to accurately model an edit's intended scope.
We propose Semi-Parametric Editing with a Retrieval-Augmented Counterfactual Model (SERAC)
SERAC stores edits in an explicit memory and learns to reason over them to modulate the base model's predictions as needed.
arXiv Detail & Related papers (2022-06-13T23:40:34Z) - Learning to Model Editing Processes [98.11448946134894]
We propose modeling editing processes, modeling the whole process of iteratively generating sequences.
We form a conceptual framework to describe the likelihood of multi-step edits, and describe neural models that can learn a generative model of sequences based on these multistep edits.
arXiv Detail & Related papers (2022-05-24T21:32:52Z) - A Structural Model for Contextual Code Changes [20.185486717922615]
Given a code snippet that is partially edited, our goal is to predict a completion of the edit for the rest of the snippet.
Our model achieves a 28% relative gain over state-of-the-art sequential models and 2x higher accuracy than syntactic models that learn to generate the edited code.
arXiv Detail & Related papers (2020-05-27T07:16:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.