MEMOIR: Lifelong Model Editing with Minimal Overwrite and Informed Retention for LLMs

Abstract: LLMs deployed in real-world systems often require post-hoc updates to incorporate new or corrected knowledge. However, editing such models efficiently and reliably - without retraining or forgetting previous information - remains a major challenge. Existing methods for lifelong model editing either compromise generalization, interfere with past edits, or fail to scale to long editing sequences. We propose MEMOIR, a novel scalable framework that injects knowledge through a residual memory, i.e., a dedicated parameter module, while preserving the core capabilities of the pre-trained model. By sparsifying input activations through sample-dependent masks, MEMOIR confines each edit to a distinct subset of the memory parameters, minimizing interference among edits. At inference, it identifies relevant edits by comparing the sparse activation patterns of new queries to those stored during editing. This enables generalization to rephrased queries by activating only the relevant knowledge while suppressing unnecessary memory activation for unrelated prompts. Experiments on question answering, hallucination correction, and out-of-distribution generalization benchmarks across LLaMA-3 and Mistral demonstrate that MEMOIR achieves state-of-the-art performance across reliability, generalization, and locality metrics, scaling to thousands of sequential edits with minimal forgetting.

• The paper presents MEMOIR, which enables lifelong model editing in LLMs by integrating residual memory for minimal overwrite.
• It employs sparsity and isolation techniques to update specific model parameters while preventing interference with pre-trained knowledge.
• Empirical results show state-of-the-art performance in reliability, generalization, and locality across thousands of edits without catastrophic forgetting.

Lifelong Model Editing with MEMOIR: A Detailed Examination

The paper "MEMOIR: Lifelong Model Editing with Minimal Overwrite and Informed Retention for LLMs," presents an innovative framework designed to address the challenge of updating LLMs efficiently and reliably without the need for retraining. This research addresses a critical need due to LLMs' propensity to produce outdated or inaccurate information and the cost of fine-tuning these models.

Core Contributions

The authors tackle the problem of lifelong model editing by introducing MEMOIR (Model Editing with Minimal Overwrite and Informed Retention), a scalable framework tailored for LLMs. The framework is predicated on three principal concepts:

1. Residual Memory: MEMOIR employs a residual memory component that integrates knowledge into a parameter module while maintaining the core pre-trained capabilities of the model. This design aims to handle knowledge injection distinctly from the pre-existing model's parameter space.
2. Sparsity and Isolation: Instead of a blanket edit across the entire parameter space, MEMOIR confines each edit to a sparse, sample-dependent activation of memory parameters. This technique, achieved through sample-dependent masks, mitigates interference among edits, thereby minimizing overwrite and catastrophic forgetting.
3. Informed Retention: At inference, MEMOIR identifies the applicability of stored edits by comparing sparse activation patterns of new queries with those created during the editing process. This feature facilitates generalization to rephrased queries, as MEMOIR activates only pertinent knowledge while suppressing unrelated memory activations.

Empirical Evaluation and Performance

The framework was rigorously evaluated on tasks such as question answering, hallucination correction, and out-of-distribution (OOD) generalization benchmarks with LLMs like LLaMA-3 and Mistral. The experiments demonstrated MEMOIR's ability to achieve state-of-the-art performance across reliability, generalization, and locality metrics. The model proved adept at managing sequences of thousands of edits with minimal forgetting.

• Reliability: MEMOIR consistently corrected models’ responses, achieving near-perfect reliability across extensive editing tasks.
• Generalization: By enabling the activation of relevant but distinct knowledge, MEMOIR showed substantial improvement in generalization, notably in rephrased query scenarios.
• Locality: The framework maintained minimal interference with non-related prompts, preserving the model’s original competencies rigorously.

Implications and Future Directions

MEMOIR's framework offers significant theoretical and practical implications. Theoretically, it proposes a robust approach to LLM model editing that balances new knowledge integration with previous knowledge retention. Practically, it presents a viable alternative to expensive retraining processes for LLMs, which can be especially valuable in applications requiring frequent updates or corrections.

Moving forward, potential enhancements could involve extending MEMOIR's application to more complex multi-modal and hierarchical models, adaptive thresholding for memory activation, or further refinement of sparse activation techniques to enhance efficiency and scalability. This research opens avenues for more flexible, efficient updates across various AI systems, crucial for applications needing constant information updates or maintaining up-to-date knowledge bases.

In conclusion, MEMOIR integrates novel strategies for lifelong model editing in LLMs, emphasizing minimal overwrite and informed retention to enhance model reliability, generalization, and locality across extensive sequences of updates. Its robust results signal a significant step forward in the field of AI model updating, promising major improvements in how models adapt to evolving datasets and knowledge.