O-Edit: Orthogonal Subspace Editing for Language Model Sequential Editing

Authors: Cai Yuchen, Ding Cao, Xinyuan Liu, Guiquan Liu
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: large language model, model editing, sequential editing

Abstract:
Large language models (LLMs) acquire knowledge during pre-training, but over time, this knowledge may become incorrect or outdated, necessitating updates after training. Knowledge editing techniques address this issue without the need for costly re-training. However, most existing methods are designed for single edits, and as the number of edits increases, they often cause a decline in the model's overall performance, posing significant challenges for sequential editing. To overcome this, we propose Orthogonal Subspace Editing, O-Edit. This algorithm orthogonalizes the direction of each knowledge update, minimizing interference between successive updates and reducing the impact of new updates on unrelated knowledge. Our approach does not require replaying previously edited data and processes each edit knowledge on time. It can perform thousands of edits on mainstream LLMs, achieving an average performance improvement that is 4.2 times better than existing methods while effectively preserving the model's performance on downstream tasks, all with minimal additional parameter overhead.

Decision: reject
Reviewer scores: 5, 6, 6

Reviews and discussion:
<thread>
creation date:
1730267917215

thread_type:
official_review

<thread_object>
creator: reviewer_qfpa

<content>
summary:
This paper proposes a novel approach to continuous editing by orthogonalizing the direction of each knowledge update, thereby minimizing interference between successive updates and reducing the impact of new updates on unrelated knowledge. This results in performance improvements while effectively maintaining the model’s performance on downstream tasks.

strengths:
- This paper proposed an innovative knowledge editing approach to address the challenge of continuous editing.
- Continuous editing of the model is achieved without introducing additional parameters.
- The theoretical derivations are detailed and convincing.

weaknesses:
- The primary experiment was conducted using only CounterFact dataset, without performing tests on a broader set of datasets.
- There is no further elaboration on the time and computational costs associated with O-Edit.
- It would be beneficial to analyze and compare the advantages of O-Edit over other knowledge editing methods such as WiSE and GRACE.
- I noticed that generalization is reduced at T=200. I think this is due to your constraints leading to insufficient thoroughness in editing, which increases generation and decreases localization.
- The theoretical validity of the CGS needs further exploration, such as investigating the relationship between CGS and the offset of the model's hidden state.

questions:
- I found that as the number of edits increases, the loc metric significantly decreases. Is this because the 7B model has a smaller dimensionality? Could you investigate the relationship between O-Edit's loc and the number of edits with different model sizes?
- Additionally, what are your thoughts on the fact that different vectors are almost orthogonal at higher dimensions? Does this mean that the effect is not significant in larger models?
- For methods like ROME and MEMIT that modify model parameters, they usually have better portability. Could you explore how the portability of your method changes after multiple edits?

In summary, this paper presents a significant advancement in sequential knowledge editing by proposing an orthogonal subspace approach. However, expanding the evaluation to more datasets, detailing computational costs, and exploring broader theoretical insights would enhance the paper’s depth and practical relevance. Additionally, addressing the questions raised could provide valuable insights for future work. Overall, this is a promising direction, deserving further investigation.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730363646015

thread_type:
official_review

<thread_object>
creator: reviewer_ttv1

<content>
summary:
The author finds that the update weights of existing knowledge editing methods are within low-rank subspaces. Based on this, the author proposed two knowledge editing methods (O-Edit & O-Edit+) based on orthogonal subspaces to handle sequence editing. O-Edit and O-Edit+ both aim to ensure that the current updates aligns vertically with the previous updates and the original knowledge. O-Edit introduces new loss function to ensure orthogonality, while O-Edit+ directly guarantees orthogonality between different knowledge. The author demonstrates through experiments that ensuring the orthogonality of knowledge helps improve the performance of existing methods in sequence editing.

strengths:
* Logical consistency: based on the assumption that knowledge from orthogonal domains has minimal mutual influence, the author has proposed two methods, O-Edit and O-Edit+, respectively. Furthermore, O-Edit+ ensures a stronger orthogonality between different knowledge. Experimental results also demonstrate that O-Edit+ exhibits superior performance.
* Innovative: the use of orthogonal subspaces to enhance sequence editing is not only novel but also intuitive.
* The ablation study demonstrates that the method proposed in the paper indeed brings performance improvement.

weaknesses:
**Main Weaknesses**

The effectiveness of O-Edit and O-Edit+ in sequence editing needs further validation.

* *W1*: In Line 51, the author says "there is still no effective solution to these problems". However, some progress [1, 2] has been made in sequence editing. I suggest the author compares these methods to demonstrate the effectiveness of O-Edit and O-Edit+ in sequence editing.
* *W2*: I suggest the author to increase the number of sequence edits T, in order to explore the limits of O-Edit and O-Edit+. GRACE [1] can make the number of editing to 3,000.

**Minor Weaknesses**
* *W3*: Previous work [3] has found that increase in the norm of edited parameters leads to a decrease in model performance and edit failures. Therefore, I suggest conducting additional experiments to demonstrate that O-Edit and O-Edit+ can suppress the increase in the weight norm.

**Missing References**
* A Survey on Knowledge Editing of Neural Networks. (2023)
* Editing Large Language Models: Problems, Methods, and Opportunities. (2023)



$Ref$:

[1] Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors. (2023)

[2] WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models. (2024)

[3] Model Editing at Scale leads to Gradual and Catastrophic Forgetting. (2024)

questions:
**Main Questions**
* *Q1*: There is a research [1] indicating that existing editing methods are not suitable for multi-hop editing. This could be due to conflicts between new and old knowledge. So, I have the following question: can O-Edit and O-Edit+ in this article be applied to multi-hop editing tasks with orthogonal subspace?
* *Q2*: I want to delve deeper into the issue of forgetting previous edited knowledge in O-Edit and O-Edit+. I hope the author studies the issue of forgetting previous edited knowledge when editing up to 1500 pieces of knowledge.

**Minor Questions**
* *Q3*: Do O-Edit and O-Edit+ incur additional time costs?


$Ref$:

[1]  Mquake: Assessing knowledge editing in language models via multi-hop questions. (2023)
</content>
</thread_object>

</thread>
<thread>
creation date:
1730612254108

thread_type:
official_review

<thread_object>
creator: reviewer_nxnx

<content>
summary:
This paper introduces O-Edit and O-Edit+, two methods for sequential knowledge editing in large language models (LLMs) that address the challenge of catastrophic forgetting during multiple edits. The key idea lies in performing edits in orthogonal subspaces, ensuring that new knowledge updates minimally interfere with both previously edited knowledge and the model's implicit knowledge. The methods work by projecting update directions into orthogonal subspaces and using post-processing techniques to maintain complete orthogonality between different knowledge updates. Through extensive experiments on Mistral-7B and Llama3-8B models using the COUNTERFACT and ZsRE datasets, the authors demonstrate that their approaches significantly outperform existing methods like ROME and MEMIT, especially when handling large numbers of sequential edits (up to 1,500). The methods also better preserve model performance on downstream tasks while requiring minimal additional parameters. The paper provides theoretical and experimental evidence showing that strong orthogonality between update matrices is crucial for successful sequential editing, offering a promising direction for future research in this area.

strengths:
1. The results look excellent compared to previous approaches.
2. The paper is well-written and easy to follow.
3. The analysis is comprehensive, which helps to understand the key insights of the proposed method.

weaknesses:
1. The method is only evaluated on two datasets. It would be interesting to see results on more scenarios where knowledge editing is important.
2. Although the idea of orthogonal subspace editing looks interesting, I am wondering is it possible to ensure the orthogonality of the direction of each knowledge update when the editing number scales to the millions level? In updating a pre-training LLM, I think it is meaningful to investigate knowledge editing where the training corpus is so large that the number of edits is hard to count.

questions:
Have you explored the influences of context lengths on knowledge editing?
</content>
</thread_object>

</thread>
<thread>
creation date:
1732615514962

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Summary of Revisions

comment:
As the deadline for submitting the revised version approaches, we have summarized the updates made to our work to enable the reviewers and Chairs to reasonably evaluate our contributions. Below are the key improvements and differences compared to the initial submission:  
﻿
1. **Expanded Dataset Comparisons**:  
- The number of datasets has been increased from 2 to 4.  
- Evaluation metrics have been expanded from 3 to 8, including new metrics for inference capability, portability, and forgetting.  
﻿
2. **Broader Model Coverage**:  
- We extended the range of evaluated models from 0.1B to 8B parameters.  
- Scaling laws for continuous editing were revealed, demonstrating the scalability of our proposed method.  
﻿
3. **Increased Editing Frequency**:  
- The editing frequency was increased to 3000 edits, and experiments with up to 100 knowledge updates per edit were conducted.  
﻿
4. **Enhanced Baseline Comparisons**:  
- We included additional baselines (FT, FT-EWC, MEND, WISE) for a more comprehensive comparison of various aspects.  
﻿
5. **Analysis of Editing Capability Before Forgetting**:  
- We investigated how different editing methods retain knowledge from different editing stages. and demonstrated our methods improved performance on all relationships.  
﻿
6. **Impact on Matrix L2 Properties**:  
- Analyzed how our method affects the L2 norm of matrices during editing.  
﻿
7. **Token-Level Analysis**:  
- Investigated the performance advantage of the orthogonal method, showing that it minimally impacts subsequent tokens from a relationship token perspective.  
﻿
8. **Computation Time Analysis**:  
- Evaluated the computational cost of the method, highlighting that computation time decreases as batch size increases.  
﻿

**We believe that the final revised version has addressed most of the issues raised by all reviewers.**
﻿


**Acknowledgment**

**We would like to sincerely thank the reviewers, especially TtV1, qFpa and NXNx, for their thoughtful feedback and valuable suggestions, which have greatly improved the quality and clarity of our work.**

**By the way, our paper withdrawal was not due to a lack of confidence in our work or dissatisfaction with the conference, but rather due to an accidental mistake during the check process. We greatly appreciate the chair’s decision to give us another chance after the withdrawal and reversion of withdrawn submission. For this conference and the rebuttal phase, we have made the almost effort we could.**
</content>
</thread_object>

</thread>


Metareview:
metareview:
This paper presents O-Edit and O-Edit+, two methods for sequential knowledge editing in large language models (LLMs) that mitigate catastrophic forgetting during multiple edits.  The approaches involve projecting update directions into orthogonal subspaces and employing post-processing to maintain strict orthogonality between updates. Extensive experiments on Mistral-7B and Llama3-8B models using the COUNTERFACT and ZsRE datasets show that O-Edit and O-Edit+ outperform baselines like ROME and MEMIT, particularly in handling up to 1,500 sequential edits. Theoretical and experimental results underscore the importance of strong orthogonality in update matrices, highlighting a promising avenue for sequential knowledge editing in LLMs.  While the idea of projecting update directions into orthogonal subspaces is promising, achieving fully orthogonal subspaces within large language models remains a significant technical challenge. Given the rapid advancements in this field, the authors are encouraged to revise the paper thoughtfully based on the reviewers' feedback.