A Survey on Knowledge Editing of Neural Networks
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View abstract on PubMed
Summary
This summary is machine-generated.Knowledge editing (KE) offers efficient adaptation for artificial neural networks (ANNs) facing evolving data. This research surveys KE methods to update ANNs without performance degradation on prior tasks, addressing catastrophic forgetting.
Area Of Science
- Artificial Intelligence
- Machine Learning
- Deep Learning
Background
- Deep neural networks (ANNs) excel in many tasks but struggle with evolving data and catastrophic forgetting.
- Updating ANNs often requires costly full retraining, hindering adaptation to new information.
Purpose Of The Study
- To survey the emerging field of knowledge editing (KE) for neural networks.
- To provide a framework for understanding KE and differentiate it from continuous learning.
- To review current KE approaches, datasets, and future research directions.
Main Methods
- Categorization of KE approaches into four families: regularization, meta-learning, direct model editing, and architectural strategies.
- Formalization of the neural network editing problem.
- Review of existing KE datasets and their relevance.
Main Results
- Knowledge editing enables efficient and targeted updates to pretrained models.
- KE aims to modify specific knowledge without compromising performance on previous tasks.
- The survey consolidates diverse KE techniques for a clearer research landscape.
Conclusions
- Knowledge editing is a crucial area for adapting ANNs to dynamic real-world data.
- Efficient adaptation methods are necessary due to the limitations of full model retraining.
- Further research in KE can lead to more robust and adaptable AI systems.
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