A language model is being pre-trained using a denoising objective. Instead of consistently using a single method to corrupt the input text (e.g., always masking tokens), the training process is configured to randomly apply one of several different corruption methods (masking, token replacement, or reordering) to each training example. Analyze the primary advantage of this mixed-method approach compared to relying on only one type of corruption throughout the entire pre-training phase.