Reasons and Solutions for the Decline in Model Performance after Editing

Summary

This paper addresses the challenges associated with the decline in performance of LLMs after undergoing knowledge editing. The study identifies the primary factors contributing to performance degradation from both data and model perspectives. By constructing a Multi-Question Dataset (MQD) and analyzing the impact of editing objectives, token length, and diversity, the paper finds that perplexity associated with editing objectives significantly affects model performance. From the model perspective, a strong correlation was observed between the L1 norm of parameter layers and editing accuracy. The paper proposes a novel method called Dump for sequence (D4C), which effectively manages the parameter growth and improves model performance post-editing.

Strengths

- Innovative Methodological Approach: The study introduces a new method, D4C, which addresses the explosive growth in parameter norms and optimizes model performance post-editing. This approach is both innovative and practical for managing edited models. - Comprehensive Data Analysis: The construction of the Multi-Question Dataset and detailed analysis of how different types of data affect model performance provide valuable insights into the mechanics of model editing. - Clear Identification of Problems and Solutions: The paper clearly identifies specific problems associated with knowledge editing in LLMs, such as catastrophic forgetting and performance bottlenecks, and provides targeted solutions to these issues. - Empirical Validation: The experiments conducted in this paper offer empirical evidence supporting the proposed methods, enhancing the credibility and applicability of the findings.

Weaknesses

- Generalizability of Findings: The study focuses on specific scenarios and datasets, which may limit the generalizability of the findings across different types of LLMs or editing tasks. - Potential Overfitting to Edited Scenarios: There is a risk that the model may become overly optimized for the edited scenarios, potentially affecting its performance on unedited or unrelated tasks. - Complexity of Implementation: The proposed D4C method, while effective, may be complex to implement and integrate into existing systems due to its sophisticated handling of parameter layers. - Unsuitable Citation Format: The citations in this paper are in the format of “XXX et al. [YEAR]”, which are not suitable enough, and had better change into the format of [1], [2], [3], ……

Questions

- Adaptability of D4C Method: How adaptable is the D4C method to different types of LLMs and knowledge editing tasks beyond those tested in your experiments? - Impact on Unedited Model Performance: How does the D4C method affect the performance of the model on tasks that have not been edited? Is there any evidence of performance trade-offs? - Handling of Diverse Editing Objectives: Could you elaborate on how the D4C method manages the complexity and diversity of editing objectives without compromising the model’s overall integrity and coherence? **Missing References** - Editing Large Language Models: Problems, Methods, and Opportunities (EMNLP 2023) - Knowledge Editing for Large Language Models: A Survey (2023) - A Survey on Knowledge Editing of Neural Networks (2023) - A Comprehensive Study of Knowledge Editing for Large Language Models (2024)

Rating

6

Confidence

3

Soundness

3

Presentation

2

Contribution

3

Limitations

- Dependency on Specific Data Characteristics: The effectiveness of the proposed solutions may depend heavily on the characteristics of the data used for training and testing, which might not be consistent across different domains or applications. - Evaluation Metrics: While the paper introduces new evaluation methods, the reliance on perplexity (PPL) and L1 norm metrics might not completely capture all aspects of model performance and health, especially in nuanced or context-dependent scenarios. - Limited Experimentation: The experiments (Section 5) in this paper are too limited to demonstrate the conclusion. - Scope of Editing Objectives: The study might not fully capture the impact of highly diverse or complex editing objectives that could be encountered in real-world scenarios.

Summary

Recent research has shown varying degrees of decline in model performance following small changes made by certain model editing methods. This paper is the first to comprehensively analyze the reasons behind such performance declines. Through extensive experiments, it identifies two main factors: data and model. For data-specific factors, the paper finds that perplexity and token length significantly influence performance. For model-specific factors, the L1 norm of the edited layer is identified as a key influence. Building upon these insights, the paper proposes a method named Dump for sequence (D4C), which significantly improves model performance.

Strengths

- The paper is well-motivated: Exploring the reasons behind and impact of small changes made by model editing techniques on the performance of unedited samples is of great significance. - The analysis of the data-specific and model-specific factors is supported with diverse datasets and comprehensive experiments. The model-specific analysis, in particular, is evaluated rigorously, addressing the forgetting issue that prior works often overlooked - The observation of the influence of editing on the model norm is intriguing. High-norm parameters can be sensitive to noise and numerically unstable. It would be beneficial if the authors could also provide an L2-norm plot for comparison. - The experimental results are impressive, demonstrating significant improvements and validating the effectiveness of the proposed method.

Weaknesses

- My main concern with the data-specific analysis is whether the conclusion is about correlation or causation. Many variables can be changed about the input data. Plotting a single Figure 3 might be insufficient to justify that perplexity and token length are the main reasons for the decline in model performance after editing. - Unfortunately, the constructed dataset is not open-sourced. - Recent research [1] has shown that model editing methods (e.g. ROME, MEMIT) are not good at handling multi-hop questions, how would D4C perform in such more challenging scenarios? - Some theoretical analysis can be conducted to demonstrate that D4C does not lead to an increase in norms. [1] Mquake: Assessing knowledge editing in language models via multi-hop questions. EMNLP 2023

Questions

- Can the authors add a section in the appendix to expand on the dataset mentioned in 3.1 (i.e., provide examples and details about the editing objectives) for better readability? - What dataset was employed in Section 5? - I encourage the authors to release the full code to enhance reproducibility. - (Minor) Consider reducing v-space in some parts of the paper (e.g., the bottom of page 2).

Rating

8

Confidence

4

Soundness

3

Presentation

4

Contribution

3

Limitations

The limitations are discussed in the paper.

Summary

The paper investigates the reasons behind performance decline in sequential model editing approaches that selectively update parameters based on both data and model factors. To address the issues causing this decline, the authors propose a method to save editing history, thereby transforming sequential editing into batch editing with minimal computational overhead.

Strengths

Extensive experimentation is conducted to empirically demonstrate how factors such as dataset characteristics, editing objectives, and model-specific properties affect performance in sequential model editing. A simple matrix storage solution is introduced, which enables the conversion of sequential editing into batch editing.

Weaknesses

The study is restricted to two closely related editing approaches. Experimentation is limited in demonstrating the efficacy of the D4C method. Different datasets and a larger number of edits for a more thorough evaluation are needed.

Questions

N/A

Rating

7

Confidence

3

Soundness

3

Presentation

3

Contribution

3

Limitations

N/A

Summary

This paper investigates the reasons and solutions for the decline in model performance of model editing. The authors conduct experiments from two perspectives: data and model. Specifically, to clarify the impact of data on the performance of edited models, the authors first evaluate how editing different types of data affects model performance. Then, the authors construct a Multi-Question Dataset (MQD) and identified that the performance of the edited models is primarily influenced by the diversity of the editing objectives and the length of the tokens. Secondly, the authors explore the factors that affect model performance from a model perspective. Experiments revealed a strong correlation between the L1 norm of the edited model layers and the editing accuracy, and identified an editing quantity bottleneck. To enhance the performance of edited models, the authors propose a Dump for sequence (D4C) method that effectively improves the performance of edited models and overcomes the previous editing bottleneck issue. This method allows for multiple effective edits with minimal impact on model performance.

Strengths

This paper investigates the impact of data on the performance of edited models. Evaluations are conducted across multiple tasks, revealing that the editing objective is the primary factor influencing model performance. The authors found that the decline in edited model performance is correlated with the explosive growth of the L1 norm of parameter layers during the editing process. This paper proposes a caching sequence edit method that leverages O(1) space complexity to retain past knowledge and regulate the explosive growth of the parameter layer norm.

Weaknesses

The writing of this paper should be improved. There is no overview of this paper, which makes it hard to follow the details of Section 3 and 4. The motivation of the proposed method is not clear. There are many typos such as line 182. There are many missing references such as: Knowledge Editing for Large Language Models: A Survey Stable Knowledge Editing in Large Language Models A Comprehensive Study of Knowledge Editing for Large Language Models Editing Large Language Models: Problems, Methods, and Opportunities

Questions

See weaknesses.

Rating

6

Confidence

3

Soundness

2

Presentation

2

Contribution

2

Limitations

Yes The limitations are on page ten. I am unsure if this counts as exceeding the page limit.