Considering time and resources, mixed fine-tuning appears preferable due to its 40 times shorter training time and comparable performance to multi-task learning, especially if planning to frequently retrain the model as new data becomes available. Ensemble decoding employs two models to make the prediction, and therefore, it takes longer at inference than the other three methods.