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Achieving over 5% improvement in coreference resolution without the need for resource-intensive training on target datasets could revolutionize multimodal AI applications.
Weight regularization, often overlooked in parameter-efficient continual learning, can still significantly improve the stability-plasticity trade-off, even when using low-rank adapters.
