Lifelong Knowledge Editing requires Better Regularization (2502.01636v2)

Abstract: Knowledge editing is a promising way to improve factuality in LLMs, but recent studies have shown significant model degradation during sequential editing. In this paper, we formalize the popular locate-then-edit methods as a two-step fine-tuning process, allowing us to precisely identify the root cause of this degradation. We show that model degradation occurs due to (1) over-optimization of internal activations and (2) continuous norm-growth of edited matrices. To mitigate these issues, we introduce two regularization techniques: (1) Most-Probable Early Stopping (MPES) and (2) explicit Frobenius norm-constraint. We demonstrate that applying these simple yet effective regularization techniques at key points in the editing process can substantially mitigate model degradation. Combining these regularization methods enables scaling locate-then-edit methods to 10,000 edits while reducing editing time by 42-61%. These results show that targeted regularization is essential for lifelong knowledge editing.

• The paper introduces ENCORE, a novel approach that mitigates overfitting and unchecked norm growth during sequential knowledge edits.
• It employs Most-Probable Early Stopping (MPES) to significantly reduce editing times by up to 76% while maintaining natural probability distributions.
• Empirical evaluations demonstrate that ENCORE outperforms methods like MEMIT and AlphaEdit, sustaining model performance over 10,000 edits.

Analyzing ENCORE: Enhancements in Lifelong Sequential Knowledge Editing

Abstract and Contribution Overview

The paper "Lifelong Sequential Knowledge Editing without Model Degradation" addresses the challenges inherent in large-scale knowledge editing within LLMs, specifically the degradation of model performance after successive edits. The research introduces ENCORE (Early stopping and Norm-Constrained Robust knowledge Editing), a more refined technique in the domain of knowledge editing aimed at ameliorating these challenges, particularly overfitting and uncontrolled growth of matrix norms. These advancements are validated through an extensive evaluation across various models such as GPT2-XL, Llama-2-7B, and Llama-3-8B, demonstrating significant improvements in editing capacity and computational efficiency.

Challenges in Knowledge Editing

Prior methods in the field, notably ROME, MEMIT, and AlphaEdit, struggle with maintaining downstream model performance when subjected to extensive sequential edits. The paper posits that overfitting on edited facts and the unchecked increase in matrix norm during editing operations largely cause such performance deterioration. Previous methods resort to importance hacking where the edited layers overshadow contributions from other parts of the model, consequently causing loss in general abilities required for varied downstream tasks.

Innovative Methodological Insights

ENCORE is introduced as a solution to these problems by incorporating two major interventions:

1. Most-Probable Early Stopping (MPES): This criterion halts the gradient descent process once the edited fact becomes the most probable output across multiple contexts. MPES ensures that edits do not lead to over-optimization, a common issue in previous methods resulting in abnormal probability distributions for edited facts. MPES curtails editing times significantly — up to 76% — while maintaining a natural probability distribution for facts.
2. Norm-Constrained Objective: To mitigate the growth in the norm of the edited matrices, a Frobenius norm constraint is included to the objective. This strategic move prevents the growing dominance of particular vectors in the model's residual stream, thus maintaining the integrity and balance of contributions from various layers throughout the editing process.

Empirical Validation and Evaluation Metrics

ENCORE is benchmarked against the established locate-then-edit methods on tasks spanning several performance metrics including edit success, paraphrase robustness, and neighborhood effect. Results indicate ENCORE's capacity for handling 10,000 sequential edits without significant loss in downstream efficacy. By constraining norm growth, the ENCORE approach preserves fluency and accuracy in model responses post-edit.

The empirical robustness of ENCORE is further highlighted by its editing speeds, outperforming MEMIT and AlphaEdit by 61% and 64% faster execution times respectively. This efficiency paves the way for more frequent and scalable model updates, essential for tracking transient knowledge paradigms in real-time applications.

Implications and Future Directions

The proposed method pushes the boundaries of current knowledge editing frameworks, suggesting a viable road ahead for sustainable and repetitive model updates. With enhanced control over norm growth and reduction in overfitting, ENCORE serves as a more stable bridge towards future developments in lifelong learning paradigms.

In conclusion, the paper provides a substantial contribution to state-of-the-art knowledge editing by unveiling critical insights into the mechanisms of matrix edits and improving upon computational efficiencies. Future research should explore adaptive norm constraints across various architectures and test ENCORE's robustness under distinct real-world editing scenarios, potentially expanding its applicability to a broader range of domain-specific models.