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🤯 Did You Know (click to read)
Sparse attention reduces memory usage from quadratic to linear in sequence length, enabling long-range dependency modeling.
Longformer introduces sparse attention mechanisms that reduce computational complexity for long sequences. This allows Transformers to process documents with thousands of tokens, maintaining context for abstractive summarization and document understanding tasks.
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💥 Impact (click to read)
Long document summarization aids research, journalism, and legal workflows by condensing information while preserving meaning.
Students and professionals can quickly extract key insights from large texts, improving efficiency and comprehension.
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