A technique for modifying a matrix by including a matrix whose rank is one. This operation, within the context of pure language processing, generally serves as an environment friendly method to refine current phrase embeddings or mannequin parameters primarily based on new data or particular coaching aims. For example, it could alter a phrase embedding matrix to mirror newly realized relationships between phrases or to include domain-specific data, achieved by altering the matrix with the outer product of two vectors. This adjustment represents a focused modification to the matrix, specializing in specific relationships reasonably than a world transformation.
The utility of this method stems from its computational effectivity and its capability to make fine-grained changes to fashions. It permits for incremental studying and adaptation, preserving beforehand realized data whereas incorporating new knowledge. Traditionally, these updates have been utilized to handle points comparable to catastrophic forgetting in neural networks and to effectively fine-tune pre-trained language fashions for particular duties. The restricted computational value related to it makes it a invaluable software when sources are constrained or fast mannequin adaptation is required.
