Title: Quiz 11 — Catastrophic Forgetting, Adapter Merging, and Cloud Training
Course: AIINFRA 201: Model Adaptation — Fine-Tuning & Quantization > Section: Week 11 — Catastrophic Forgetting, Adapter Merging, and Cloud Training

Q: Catastrophic forgetting severity in LLMs decreases as model scale increases within the 1B–7B parameter range.
- True
- False
A: False

Q: Which of the following is NOT one of the three mechanistic causes of catastrophic forgetting discussed this week?
- Gradient interference in attention weights
- Representational drift in intermediate layers
- Flattening of the loss landscape around the prior task's minimum
- Reduction in total parameter count during fine-tuning
A: Reduction in total parameter count during fine-tuning

Q: Name the four families of catastrophic-forgetting mitigation strategies.
Feedback: Sample/expected answer: Replay-based, regularization-based, gradient-based, and architecture-based

Q: Why does LoRA often resist catastrophic forgetting better than full fine-tuning?
- It trains for fewer total steps than full fine-tuning
- It isolates changes to small adapter matrices while keeping the base model frozen
- It automatically replays old-task data during training
- It uses a larger learning rate that converges faster
A: It isolates changes to small adapter matrices while keeping the base model frozen

Q: LoRA's resistance to catastrophic forgetting is universal and does not depend on the magnitude of the domain shift between the old and new tasks.
- True
- False
A: False

Q: What does mergekit allow you to do with a fully fine-tuned model?
- Extract a LoRA adapter by diffing the base and tuned weights
- Automatically generate new training data
- Quantize the model to 4-bit precision
- Convert the model to ONNX format
A: Extract a LoRA adapter by diffing the base and tuned weights

Q: Name the two adapter-merging methods introduced in the PEFT merging blog that go beyond naive averaging.
Feedback: Sample/expected answer: TIES and DARE

Q: Merging multiple task-specific LoRA adapters into a single model is always straightforward and free of conflict, so it's generally preferable to keeping adapters separate.
- True
- False
A: False