AI BriefingHow BigBird's Attention Works
Key point
This explains the working principle and efficiency of Block Sparse Attention, the core of the BigBird model.
Details
This covers the mechanism of the BigBird model, which addresses the chronic problem of Transformer models: $O(n^2)$ complexity with respect to sequence length ($n$).
BigBird introduces Block Sparse Attention to dramatically lower computational cost while still handling long sequences of up to 4,096 tokens. Through this, it achieved SOTA performance in long document summarization and long-context question answering (QA).
The model focuses on efficiently approximating BERT's Full Attention, and is readily available as the BigBird RoBERTa-like model via Hugging Face Transformers.
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