Catastrophic forgetting occurs when neural networks lose previously learned knowledge while training on new data. Mechanism: Gradient updates for new task overwrite weights important for old tasks. Network doesn't distinguish between general knowledge and task-specific weights. Symptoms: Model excels at new task but fails at capabilities it previously had. Common when fine-tuning pretrained models on narrow domains. Mitigation strategies: Elastic Weight Consolidation (EWC) - penalize changes to important weights, memory replay - train on samples from previous tasks, progressive networks - add new capacity without overwriting, PEFT methods - freeze base model and train adapters, regularization techniques. In LLM fine-tuning: Aggressive learning rates cause forgetting, train on mixed data (old + new), use LoRA to preserve base capabilities. Detection: Evaluate on held-out benchmarks from original training distribution. Practical advice: Lower learning rates, shorter training, mix in instruction-following data, validate against base model capabilities regularly. Understanding forgetting dynamics is crucial for maintaining model quality during adaptation.