When we try to scale Transformer models to handle long sequences, they hit their computational ceiling far earlier than one might expect, leading to performance issues.
Self-attention allows Transformers to comprehend relationships between distant elements in sequences, but the memory and compute costs grow quadratically with sequence length.
The self-attention mechanism is pivotal for the capability of Transformers, yet it restricts their scalability due to the rapid increase in complexity with longer inputs.
Innovations are emerging to address the sequence length limitations in Transformers, but a rethinking of the overall approach might be necessary to truly overcome these barriers.
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