Resolving Lexical Bias in Edit Scoping with Projector Editor Networks

Authors: Hammad Rizwan, Domenic Rosati, Ga Wu, Hassan Sajjad
Primary area: generative models
Keywords: Representation Learning, Model Editing and LLM's

Abstract:
Weight-preserving large language model editing techniques rely heavily on the scoping mechanism that decides when to apply an edit to the base model. These scoping mechanisms utilize distance functions in the representation space. In this work, we show that distance-based scoping functions grapple with strong lexical biases leading to issues such as deciding that irrelevant prompts that share overlapping words should result in applying an edit. We address these problems by introducing Projector Editor Networks for Model Editing (PENME), a principled model editing approach designed to learn the optimal representation space for scoping via contrastive learning. We show that PENME achieves state of the art model editing results while being compute-efficient at inference time than previous methods and flexible enough to adapt across architectures

Decision: reject
Reviewer scores: 5, 6, 6, 5

Reviews and discussion:
<thread>
creation date:
1730067141145

thread_type:
official_review

<thread_object>
creator: reviewer_uwxu

<content>
summary:
The paper proposes to address lexical bias in continual model editing (i.e., token similarity affecting edit decisions). The framework is similar to existing clustering-based setups (e.g., GRACE [1]). This seems to be achieved by explicitly training a projection network and discouraging exploiting lexical correlations. The paper shows improved performance on CounterFact and zsRE. 

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

strengths:
The paper highlights the problem of lexical bias in clustering-based editing approaches, which can raise awareness of this particular issue.

weaknesses:
It is difficult to tell exactly what parts of the paper are novel contributions. I think the main difference is that in GRACE the cookbook representations are manually maintained whereas here they're learned. The related work section needs to tell the reader why this work is different from the previous works, not just describing them.

questions:
N/A
</content>
</thread_object>

</thread>
<thread>
creation date:
1730079335540

thread_type:
official_review

<thread_object>
creator: reviewer_hiiz

<content>
summary:
The paper introduces Projector Editor Networks for Model Editing (PENME), a novel approach to improving large language model editing techniques. PENME addresses the wrong when deal with incorrect edits on irrelevant prompts with similar words by using contrastive learning to create an optimized representation space. This space allows precise localization of edits by maintaining distance between irrelevant prompts while keeping paraphrases close. The empirical study demonstrates that PENME achieves great results in model editing.

strengths:
* Propose the lexical bias in Model editing which is a new aspect to improve the performance of model editing.
* Propose a projection network that maps the model’s representation space to a new representation space where lexical dominance is minimized

weaknesses:
* This paper suggests that lexical bias refers to different editing subjects with the same relation, such as "The twin city of Pittsburgh is" and "The twin city of Portsmouth is." However, the prevalence of such cases in the CounterFact and ZsRE datasets is unclear. 
﻿ 
* Figure 3 and Figure 7 illustrate the "Percentage of samples where edits are closer to unrelated neighbors," but this is insufficient to demonstrate lexical bias. At lower model layers, high similarity may result from underdeveloped sentence representations, while at higher layers, the reduced percentage indicates greater differentiation between sentences. 
﻿ 
* The results in Table 1 show that GRACE is a strong baseline. PENME, which extends GRACE by using a projection network to map data representations, needs to clearly highlight the differences between PENME and GRACE. 
﻿ 
* PENME focuses on addressing lexical bias, so it should perform well on Loc and Para. However, in Table 1, only Para shows improvement, which is insufficient to fully support the paper's contributions.

questions:
* It is better to give more example to shown what is  lexical bias and lexical overlap in the paper.
* Some results in Table 1 is not clear enough (close to 0.0), such as why GRACE on zsRE get the 0.00 in Loc on Llama2-7b?
* In Figure 6, it is better to add the resutls on GRACE.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730208054049

thread_type:
official_review

<thread_object>
creator: reviewer_uafd

<content>
summary:
This paper addresses an important question in model editing: the tradeoff between generalization (e.g., paraphrase handling) and locality (e.g., avoiding unintended edits on irrelevant queries). To tackle this issue, the authors propose PENME, which consists of two components: (1) a projection network trained with a contrastive objective to separate paraphrased and irrelevant prompts in the representation space, and (2) a memory-based retrieval scheme that enhances editing precision by applying a similarity threshold as a scoping mechanism. Experiments on three models demonstrate the effectiveness of PENME compared to other baselines.

strengths:
1. This paper addresses an important topic in model editing: the tradeoff between generalization and locality. Though the problem is already well-defined, the two proposed methods are simple yet effective to improve editing's effectiveness.
3. The experimental results are significant, showing improvements over several state-of-the-art editing methods.

weaknesses:
1. I don’t have strong negative feedback on this paper, but additional analyses would be helpful. See the Questions section for more details.
2. The presentation could be improved; all figures are quite blurry and lack high quality.
3. The writing, especially in the experiments section, needs clarification. The baseline setup is hard to follow as the authors haven't provided an overview of all compared baselines (e.g., MELO). For instance, it’s unclear why Llama-2-7b wasn’t tested on MELO and no T5 for MEMIT.

questions:
1. An ablation study on each proposed component would strengthen the analysis.
2. Could you provide a visualization of the representation space showing edits, paraphrases, and neighbors before and after editing? This would nicely complement the current analysis.
3. Fig6: LAMA->LLaMA
4. Adding more editing methods in the scaling experiment can help to confirm the robustness of PENME.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730369271727

thread_type:
official_review

<thread_object>
creator: reviewer_y63d

<content>
summary:
The paper points out that knowledge editing methods based on scoping mechanisms are limited by lexical bias. It proposes using a projector network to decrease the distance between edits and paraphrases, and increase the distance between edits and neighbors to address this issue.

strengths:
* The paper highlights the challenge of lexical bias in knowledge editing, providing valuable guidance for future research in this field.
* The paper proposes using a projector network to enhance retrieval within the codebook, effectively improving the generalization of edits and preventing misfires.

weaknesses:
* The authors didn't fully explain their method. In discussing the construction of key-value memory, they described how to create the keys and set the threshold but didn't explain how to obtain the corresponding values.
* The paper claims that PENME enables faster edit retrieval and simplifies edit removal or updates. However, it lacks supporting experimental evidence.
* The hyperparameter m in the loss function is crucial for the projection network's performance, yet the paper lacks ablation studies on this.
* Many methods were selected for performance comparison, but the authors did not explain why these specific methods were chosen.
* The images in the paper are disorganized and difficult to interpret. Combining multiple experiment results into single images reduces readability.
* The organization of the main text and appendix is unclear. For example, ablation experiments present results for different similarity thresholds for edit-to-edit pairings, but this hyperparameter isn't introduced in the main text, making it hard to understand.
* The paper doesn’t provide detailed explanations of the projector networks, such as their parameter dimensions.

questions:
* Why choose L2 distance in the loss function instead of cosine similarity?
* Why choose cosine similarity for edit-to-edit pairings instead of L2 distance?
* What impact does the hyperparameter m have on the projection network's performance?
* What is the architecture of the projection network? Is it similar to a feed-forward layer in a transformer?
* How is the memory value in the key-value memory obtained?
* The paper proposes two data-driven thresholding schemes. Was Option 1 chosen over Option 2 based on experimental results?
* Why were these methods chosen as baselines in the paper? Is it because they achieved state-of-the-art results on certain metrics or share similarities with PENME?
</content>
</thread_object>

</thread>
<thread>
creation date:
1732070590590

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Central Response

comment:
We would like to thank the reviewers for their valuable comments and feedback. In the following, we reiterate the contribution of our work and summarized the experiments conducted for the rebuttal. 

**Novelty:** This is the first work that identifies the issue of lexical bias in model editing. The explicit modelling of a representation space where paraphrases of edits demonstrate proximity while neighbouring examples are far away is a novel solution to the problem at hand. To the best of our knowledge, PENME is the only model editing method designed to maintain both high locality and generalization. The results in Table 1 demonstrate the efficacy of PENME where none of the other methods, whether weight-preserving or weight-modifying, improves both locality and generalization. 

**Comparison between PENME, GRACE and MELO:** Reviewer uwxU, y63D and hiiz asked about the difference between PENME and GRACE/MELO. In the following, we compare these methods and have clarified our contribution.

Cluster-based similarity systems like GRACE and MELO [1] and [2] rely on concept separability within the representation space to manually maintain keys in their codebooks. However, our analysis reveals that lexically similar prompts cluster closer to edits than their paraphrases, heightening the risk of system failure as can be seen from figure 1 and 3. Moreover, their cluster based design necessitates storing edit paraphrases as codebook entries for effective generalization which increases retrieval latency. PENME overcomes these limitations by learning a projection space that enhances representation structure, enabling more effective organization of keys for faster and accurate retrieval. Moreover, PENME consistently outperforms both weight-preserving and weight-modifying methods across various architectures, underscoring its adaptability and efficacy.

**Experiments:** We provide a detailed discussion on each experiment in our response to individual comments. In the following, we provide a summary of conducted experiments and their findings.

   1. A runtime comparison between PENME, GRACE, and MELO reveals that PENME is faster due to its requirement for fewer codebook entries. The experiment also highlights that multiple codebook entries are lost during training as a result of cluster resizing operations. 

2. Ablation on margin $m$ and the corresponding adjustments needed to $\tau$ for data-driven threshold. The results demonstrate that a value of $40 \leq m \leq 80$ provides a balanced trade-off between generalization and locality.

3. Comparison of cosine loss and contrastive loss for training the projection network. The results demonstrate that the L2 distance performs better than the cosine similarity. 

4. Token overlap metrics which showcase the lexical similarity characteristics between edits and neighbours in Counterfact and ZsRE datasets. The results indicate that this issue is not only more pronounced in Counterfact compared to ZsRE but also occurs abundantly within the Counterfact dataset. We also provide data samples from both datasets.

5. For data-driven threshold we provide the percentage of samples where training paraphrases or neighbours fail due to adjustment factor $\tau$. This provides insights for data driven thresholds  Option 1 and 2. 

---
**Summary of Revisions:** Details regarding Section numbers are provided in individual comments.
1. The structure of the Appendix has been changed to reflect the order in which it the referred to in the texts.

2. The requested ablations and visualizations have been added to the appendix.

3. Paper components for which further clarifications were requested have been added as blue text in the paper.

4. Image quality of all images has been improved. Figures 3 and 6 have been altered to improve readability.

[1] Tom Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, and Marzyeh Ghassemi. Aging with grace: Lifelong model editing with discrete key-value adaptors. Advances in Neural Information Processing Systems

[2] Lang Yu, Qin Chen, Jie Zhou, and Liang He. Melo: Enhancing model editing with neuron-indexed dynamic lora. In Proceedings of the AAAI Conference on Artificial Intelligence.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732402427204

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Comment by Authors

comment:
Dear Reviewers,

Thank you for your thorough feedback on our manuscript. We have addressed all your comments. With the rebuttal deadline approaching, we would greatly appreciate a discussion regarding our responses. Please let us know if there are any points that require further clarification or additional explanation.
 
Authors
</content>
</thread_object>

</thread>


Metareview:
metareview:
This paper explores the tradeoff between generalization (e.g., handling paraphrases) and locality (e.g., avoiding unintended edits on irrelevant queries) in model editing. The authors introduce PENME, a method with two key components: (1) a projection network trained with a contrastive objective to distinguish paraphrased from irrelevant prompts in the representation space, and (2) a memory-based retrieval scheme that improves editing precision by using a similarity threshold as a scoping mechanism. Experiments across three models show that PENME outperforms other baseline approaches.  However, the technical contribution of the work is limited, especially in comparison to previous works like GRACE, where cookbook representations are manually maintained while in this paper, they are learned. Moreover, this paper does not fully explain the methodology, particularly in the construction of key-value memory. The organization and writing of this paper can also be improved. The authors are encouraged to carefully revise the paper based on the reviewers' feedback.