A team is developing a feature to rewrite informal user-generated text into a more formal style. They are considering two deep learning-based approaches:

1. A single, large sequence-to-sequence model trained end-to-end to directly transform informal text to formal text.
2. A two-stage system where a first model generates an initial formal version ('predict'), and a second, specialized model corrects grammatical errors and improves the stylistic formality of the initial output ('refine').

Evaluate the two-stage 'predict-then-refine' approach compared to the single-model approach for this specific task. Discuss the potential trade-offs, including aspects like output quality, computational cost, and system complexity.