  Instruction Fine-Tuning (IFT) is a powerful paradigm that strengthens the
zero-shot capabilities of Large Language Models (LLMs), but in doing so induces
new evaluation metric requirements. We show LLM-based metrics to be well
adapted to these requirements, and leverage them to conduct an investigation of
task-specialization strategies, quantifying the trade-offs that emerge in
practical industrial settings. Our findings offer practitioners actionable
insights for real-world IFT model deployment.
