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

💬 Discussion 11 — Support-to-Legal Domain Adaptation (20 pts)

You've fine-tuned a model on your company's customer-support domain, and it's performing well. Now the business asks you to adapt it to handle legal-domain questions too — but you can't afford to lose quality on customer support. In a post of at least 150 words, address:

- Would you choose continual fine-tuning (keep training the same model on legal data), separate LoRA adapters (one for support, one for legal, swapped at inference time), or adapter merging (train two adapters, then merge into one model)? Justify your choice using this week's material on forgetting mechanisms and merging tradeoffs.
- What specific risks would you watch for with your chosen approach, and how would you detect them (what would you measure, and when)?
- Is there a scenario in this exact situation where you'd change your answer (e.g., inference infrastructure constraints, how different the two domains really are)?

Respond to at least two classmates — ideally engaging with someone who chose a different approach than you did — and explain where you'd push back or agree based on the tradeoffs discussed in lecture.