Unverified50% confidenceFactExact time
Updating parametric memory requires fine-tuning or continual pre-training, both of which require additional training processes and computational resources.
1
Sources
50%
Confidence
Long-term
Relevance
9/6/2026
First Seen
Sources
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Related Claims
UnverifiedMethods such as freezing LLM backbone parameters during the visual alignment training phase, staged training, data ratio replay, and LoRA can mitigate catastrophic forgetting in multimodal training.76% similarUnverifiedTrigger-based retraining (based on performance degradation monitoring and data drift detection) can replace scheduled full retraining, directing computational resources precisely where they are truly needed76% similarUnverifiedMethods for addressing overfitting include increasing training data, regularization, early stopping, and Dropout.75% similar
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