<ul><li>The Transformer model, widely used for sequence modeling, relies on self-attention which becomes inefficient for long sequences.</li><li>The ZETA method proposes leveraging Z-Order Curves for Efficient Top-k Attention to address the inefficiency of self-attention.</li><li>ZETA enables parallel querying of past tokens and achieves similar performance to self-attention while reducing computational demands.</li><li>Experimental results show that ZETA outperforms attention models on various language modeling tasks.</li></ul>

ZETA: Leveraging Z-order Curves for Efficient Top-k Attention

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