You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: After verifying on the validation set, we discoverthree patternsconsistently existing in both tasks (over 50% of important heads).This suggests that important patterns are generalizable across multiple NLP tasks, whichis consistent with the findings in Bian et al. (2021). Further analysis also shows that the attention patterns are consistent after fine-tuning, where we report an average Jensen-Shannon Divergenceof 0.01 between the attention distributions of BERTSum across 3 random seeds.We hope our findings provide motivation for the in-depth study of pattern importance in different NLP tasks. Lastly, while it may be argued that this step of the pipeline can be automated by directly evaluating the importance and relevance of predefined patterns (e.g. syntax, discourse) based on intuitions, as indicated below, our interactive approach allows the discovery of interpretable patterns which otherwise would be hard to define due to the infinite search space of possible patterns. Next, we describe the three discovered patterns in detail.

Question: How can attention patterns from larger models be applied to smaller models if the models might differ in the size and number of attention layers?
BERTSum is the summarization model, and Cross-Segment BERT is the topic segmentation model. The paper does not discuss transferring patterns onto pretrained transformers of different architecture.