Rebuilding ROME : Resolving Model Collapse during Sequential Model Editing

Abstract: Recent work using Rank-One Model Editing (ROME), a popular model editing method, has shown that there are certain facts that the algorithm is unable to edit without breaking the model. Such edits have previously been called disabling edits. These disabling edits cause immediate model collapse and limits the use of ROME for sequential editing. In this paper, we show that disabling edits are an artifact of irregularities in the implementation of ROME. With this paper, we provide a more stable implementation ROME, which we call r-ROME and show that model collapse is no longer observed when making large scale sequential edits with r-ROME, while further improving generalization and locality of model editing compared to the original implementation of ROME. We also provide a detailed mathematical explanation of the reason behind disabling edits.

• The paper demonstrates that disabling edits in ROME can be mitigated with r-ROME, preserving LLM performance during sequential modifications.
• It employs normalized entropy and update matrix norm as key metrics to diagnose model collapse and assess performance stability.
• The study highlights that dataset-specific properties, particularly from CounterFact, significantly contribute to disabling edits, guiding future dataset optimizations.

Revisiting Sequential Model Editing: A Study on Counteracting Model Collapse in ROME

Introduction

In the domain of modifying existing LLMs through parameter adjustments, sequential model editing has emerged as a critical technique for integrating multiple updates without the need to retrain the model from scratch. Previous research has highlighted the efficacy of Rank-One Model Editing (ROME), a method preferred for its direct approach to parameter modification. However, an issue identified with ROME is the phenomenon of model collapse—an abrupt deterioration in model performance following certain edits, referred to as disabling edits. This paper presents an investigation into the disabling edits associated with ROME and introduces an enhanced implementation, r-ROME, which mitigates the issue of model collapse, facilitating large-scale sequential edits.

Sequential Model Editing and its Challenges

Sequential model editing refers to the process of applying successive edits to a single model, adjusting its parameters after each modification to reflect new or updated information. This methodology has become increasingly relevant with the growing need to keep LLMs current without the prohibitive cost of retraining. However, disabling edits, as identified in prior work, severely hamper the potential of sequential editing by causing model collapse. This phenomenon manifests as a significant drop in the model's overall capabilities, including a loss of previously edited knowledge and a decrease in general performance metrics.

Investigation into Disabling Edits

The investigation begins with a detailed examination of disabling edits, employing two key metrics—normalized entropy of generated text and the norm of the update matrix ($|\Delta|$)—to identify edits that precipitate model collapse. Through extensive testing with two popular datasets, CounterFact and zsRE, the study reveals that disabling edits predominantly occur with the CounterFact dataset. This finding suggests that the specific properties of edits sourced from CounterFact, rather than intrinsic flaws in ROME itself, contribute to model collapse.

Introducing r-ROME

Upon a comprehensive review and subsequent re-implementation of the ROME algorithm, the study unveils a surprising discovery: the newly implemented version, dubbed r-ROME, does not exhibit the disabling edit phenomenon. This version demonstrates significantly reduced $|\Delta|$ values for updates, indicating milder adjustments to the model's parameters and, thereby, preventing collapse. Comparative assessments between the original ROME and r-ROME across standard model editing metrics confirm that the new implementation performs comparably well in efficacy and reliability while ensuring stability during sequential edits.

Implications and Future Directions

The development of r-ROME represents a significant step forward in the field of LLM knowledge editing, especially for applications requiring sequential updates. By circumventing the issue of model collapse, r-ROME opens new avenues for maintaining and enhancing LLMs with updated information over time. This advancement also raises intriguing questions about the underlying mechanics of model editing and the specific characteristics of datasets that may influence the stability of parameter modifications.

Given the findings, future research could explore deeper into dataset properties that align with stable editing processes, potentially leading to the development of guidelines for creating edit-friendly datasets. Moreover, the robustness of r-ROME against disabling edits invites further exploration into its scalability and applicability across a broader range of models and editing objectives.

Conclusion

The study's investigation into disabling edits within the context of ROME and the introduction of an improved implementation, r-ROME, mark important progress in the field of sequential model editing. By addressing the critical challenge of model collapse, the research paves the way for more reliable and extensive modifications to LLMs without the need for complete retraining. As the field moves forward, r-ROME stands as a testament to the evolving capabilities in model editing technology, promising enhanced flexibility and efficiency in the ongoing effort to keep LLMs both accurate and relevant.