Massive Editing for Large Language Models via Meta Learning (2311.04661v3)

Abstract: While LLMs have enabled learning knowledge from the pre-training corpora, the acquired knowledge may be fundamentally incorrect or outdated over time, which necessitates rectifying the knowledge of the LLM (LM) after the training. A promising approach involves employing a hyper-network to generate parameter shift, whereas existing hyper-networks suffer from inferior scalability in synchronous editing operation amount. To mitigate the problem, we propose the MAssive LLM Editing Network (MALMEN), which formulates the parameter shift aggregation as the least square problem, subsequently updating the LM parameters using the normal equation. To accommodate editing multiple facts simultaneously with limited memory budgets, we separate the computation on the hyper-network and LM, enabling arbitrary batch size on both neural networks. Our method is evaluated by editing up to thousands of facts on LMs with different architectures, i.e., BERT-base, GPT-2, T5-XL (2.8B), and GPT-J (6B), across various knowledge-intensive NLP tasks, i.e., closed book fact-checking and question answering. Remarkably, MALMEN is capable of editing hundreds of times more facts than strong baselines with the identical hyper-network architecture and outperforms editor specifically designed for GPT. Our code is available at https://github.com/ChenmienTan/malmen.

An Examination of "Massive Editing for LLM via Meta Learning"

The paper "Massive Editing for LLM via Meta Learning" presents a novel methodology called MAssive LLM Editing Network (MALMEN) designed to enhance the scalability, efficiency, and accuracy of editing LLMs. This research addresses a prevalent challenge in deploying LLMs: the need to rectify incorrect or outdated knowledge without comprehensive retraining, which risks overfitting and catastrophic forgetting.

Core Contributions

The primary contribution of this paper is the introduction of MALMEN, an editing technique that aggregates parameter shifts through a least squares formulation, using the normal equation to update LLM parameters. This approach allows for the simultaneous editing of a multitude of facts, a significant improvement over previous techniques like MEND, which were limited in scalability due to computational and memory expenses.

Technical Framework

1. Parameter Shift Aggregation: MALMEN addresses the issue of parameter shift cancellation by framing the aggregation as a least squares problem. This involves finding an optimal parameter shift that minimizes the discrepancy for all facts being edited, thereby ensuring statistical significance and reducing the risk of contradictory updates.
2. Memory-Efficient Backpropagation: The authors present an innovative training method that separates computation on the hyper-network and LLM. This separation facilitates the use of arbitrary batch sizes, vastly decreasing memory requirements and improving scalability. This allows MALMEN to edit more facts simultaneously than previous methods.
3. Empirical Assessment: The paper demonstrates MALMEN's effectiveness across different LLM architectures such as BERT-base, GPT-2, T5-XL, and GPT-J, and in diverse tasks including closed-book fact-checking and question answering. This diversity in testing reinforces the broad applicability of their approach.

Significant Findings and Implications

• Improved Scalability: MALMEN significantly surpasses existing methods in terms of scalability, editing hundreds of times more facts than MEND with identical hyper-network architecture. This is particularly beneficial for real-world applications where LLMs must constantly update vast quantities of domain-specific knowledge.
• Enhanced Editing Performance: The results show that MALMEN not only scales efficiently but also maintains strong performance metrics across various models and tasks, achieving high edit success, generalization success, and locality success, which establishes its robustness.
• Reduced Memory Footprint: The restructuring of computation allows MALMEN to perform thousands of edits with manageable memory usage, which is critical for deploying LLM editors on standard hardware within acceptable time constraints.

Future Directions

This paper lays the groundwork for future research in several dimensions:

• Extending Beyond Textual Edits: The approach could be adapted for multimodal LLMs which require simultaneous updates across different content types.
• Improving Generalization: Further research could focus on enhancing MALMEN's ability to generalize edits beyond minor rephrasings, potentially integrating more sophisticated semantic understanding models.
• Scalability Enhancements: While MALMEN makes significant strides, continuous improvements in editing efficiency and reducing computational overhead remain crucial, particularly for deployment in resource-constrained environments.

Conclusion

The MALMEN framework proposed by Tan et al. represents an important advancement in model editing techniques for LLMs. By providing a scalable, efficient, and accurate mechanism for updating model knowledge, this work contributes significantly to the practical application and long-term viability of LLMs in dynamic environments. As LLMs become increasingly central to AI applications, the ability to update their knowledge bases effectively will remain a pivotal area of ongoing research.