Fine-tuning breaks model general knowledge through catastrophic forgetting

Short summary

Fine-tuning a model on a specific task can cause catastrophic forgetting, where new knowledge overwrites general capabilities. Techniques like regularization, rehearsal, multi-task learning, and LoRA can mitigate but not eliminate this trade-off. The author argues specialization is often a feature rather than a bug, and future meta-learning approaches may eventually make fine-tuning obsolete.

• •Catastrophic forgetting occurs when fine-tuning overwrites general model knowledge
• •Mitigation techniques include regularization, rehearsal, multi-task learning, and LoRA
• •Specialization vs. generality is a fundamental trade-off; meta-learning may eventually resolve it

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