A machine learning team is tasked with building a robust language model ensemble for a complex question-answering system, but they have a limited budget. They are considering two primary strategies to ensure the models in their ensemble are diverse.

Strategy A: Select a single, powerful open-source base model and create three distinct versions by fine-tuning it on three different, specialized datasets (e.g., one on scientific papers, one on legal documents, and one on news articles).

Strategy B: Procure licenses for three different, pre-trained models from separate developers, each known to have a unique underlying architecture and initial training corpus.

Evaluate these two strategies. Which approach is more likely to result in a more effective and robust ensemble? Justify your decision by comparing the sources of diversity each strategy introduces.