Perturbation-Restrained Sequential Model Editing

Knowledge editing methods when applied sequentially lead to significant model degradation. In this paper, the authors identify the reasons behind the loss of model's general abilities in the form of increasing singular value of edit-matrix as a function of the number of edits, and propose methods to control that.

The research tackles the issues that arise due to weight shifts in sequential model editing when using weight-modifying approaches. The authors present a new approach, PRUNE, which aims to stabilize editing by maintaining control over these shifts. Importantly, the paper proposes an upper bound on weight modifications, beyond which the model's original capabilities degrade, resulting in reduced performance and an inability to retain edits. This bound serves as a theoretical safeguard, ensuring that model modifications remain within an optimal range for consistent, reliable performance.

This paper analyzes how the condition number of the edited matrix affects the models' sensitivity to perturbations and proposes a framework called PRUNE to impose constraints that lower this sensitivity. Experiments demonstrate that PRUNE effectively preserves the general abilities of LLMs while maintaining strong editing performance across various tasks.

This paper investigates the capability of large language models (LLMs) to preserve their general knowledge after sequential editing through a plug-and-play framework named PRUNE. The authors suggest that the condition number of the edited matrix significantly influences the general abilities of the edited models. They propose a method which involves SVD decomposition to ensure that the updated matrix maintains the singular values of the original matrix, thereby preventing substantial perturbations. The paper evaluates the effectiveness of this method across three popular editing methods and three large language models

Summary of Scientific Claims and Findings

The paper's main claim is that the degradation of general model abilities during sequential model editing is closely linked to the condition number of the edited matrix. To address this, the authors introduce PRUNE, a framework that stabilizes the impact of edits by limiting the size of perturbations. Experiments across multiple LLMs (such as GPT-2 XL, LLaMA-2, and LLaMA-3) demonstrate that PRUNE is effective in preserving model performance across a variety of tasks. The authors show that applying PRUNE limits perturbations, reducing forgetfulness and improving stability even after multiple edits.

Key findings include:

1. PRUNE improves model performance by preventing excessive perturbations during sequential edits.
2. The framework is demonstrated to be effective across different LLM architectures and tasks, maintaining generalization ability and reducing memory loss.
3. Applying PRUNE multiple times improves model performance more than applying it once, highlighting the method’s scalability.

Strengths of the Paper

1. Theoretical Contribution: The paper offers a novel approach to mitigating performance degradation during sequential model editing by incorporating perturbation theory and proposing an upper bound for weight modification. This theoretical contribution provides a clear, actionable framework for stabilizing model performance.

2. Experimental Validation: The experiments conducted on LLaMA-2 (7B) and GPT-2 XL (1.5B) demonstrate the practical utility of PRUNE in real-world settings. The paper successfully shows that the method improves model stability and preserves general abilities in the face of long-term edits.

3. Clarity of Methodology: The paper clearly explains the rationale behind PRUNE, particularly the use of a logarithmic restraining function to control the growth of the largest singular values. This offers a concrete solution to the challenge of avoiding overfitting while retaining essential knowledge.

4. Practical Implications: The results suggest that PRUNE could be of significant value for practitioners who need to perform sequential edits on large models while minimizing the risk of knowledge loss.

Weaknesses and Missing Elements

1. Limited Scope of Models and Tasks: The experiments are primarily conducted on a small set of models (LLaMA-2 and GPT-2 XL) and tasks (reasoning, summarization, question answering). While these models are well-known, the results could benefit from broader evaluation on additional model architectures (such as GPT-3, T5, or BERT) and tasks, particularly those involving domain-specific or multi-modal learning. Expanding the sample size and task variety would enhance the generalizability of the results.

2. Impact of Multiple Edits on Recent Changes: Although PRUNE is shown to be effective for most sequential edits, the efficacy of the most recent edits diminishes after many applications of the method. Further exploration into this phenomenon and the design of more targeted techniques for recent edits could improve the approach.

3. Clarification on PCA Visualizations and Baseline Comparisons: There are mentions of discrepancies in PCA visualizations and baseline performance comparisons, particularly for LLaMA on general tasks. The authors should address these concerns in more detail to clarify the results and ensure full transparency.

4. Further Exploration of Method Limitations: The paper discusses the limitations of PRUNE, particularly in scenarios where edits may still be lost despite the constraints. Expanding this discussion and exploring alternative methods for mitigating this loss would strengthen the overall contribution. For instance, analyzing how PRUNE compares with other contemporary model-editing techniques like MEMIT or ROME could offer insights into its relative advantages and disadvantages.

Decision Rationale

The paper provides a novel and significant contribution to the field of model editing, particularly for large language models. The introduction of PRUNE is theoretically sound and experimentally validated, offering a solution to an important challenge in machine learning. The strength of the paper lies in its clear methodology, thorough experimental evaluation, and potential practical impact.

However, there are some limitations, primarily the narrow scope of models and tasks, the diminishing efficacy of PRUNE for recent edits, and the need for more thorough clarification in certain experimental aspects. Despite these weaknesses, the core contribution is strong, and the framework holds promise for further research and refinement.

Based on the strengths in theoretical innovation and practical application, and considering the possibility for further improvements and future research directions, I would recommend accepting this paper, with minor revisions to address the aforementioned weaknesses.

Accept (Poster)