Longformer models use a mix of global and local attention mechanisms to process long sequences, making them suitable for tasks like document classification, summarization, and coreference resolution. Optimizing these models involves balancing computational efficiency with the need to maintain long-term context.

*What specific techniques or modifications can be applied to enhance the performance of Longformer models? *

Are there particular training strategies, pruning methods, or hardware considerations that can help achieve this balance? Insights into practical implementations and case studies where Longformer models have been successfully optimized would be highly valuable.