BERT-LSH: Reducing Absolute Compute For Attention

Zezheng Li; Kingston Yip

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arXiv · 2404.08836

BERT-LSH: Reducing Absolute Compute For Attention

Zezheng Li·Kingston Yip

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Abstract

This study introduces a novel BERT-LSH model that incorporates Locality Sensitive Hashing (LSH) to approximate the attention mechanism in the BERT architecture. We examine the computational efficiency and performance of this model compared to a standard baseline BERT model. Our findings reveal that BERT-LSH significantly reduces computational demand for the self-attention layer while unexpectedly outperforming the baseline model in pretraining and fine-tuning tasks. These results suggest that the LSH-based attention mechanism not only offers computational advantages but also may enhance the model's ability to generalize from its training data. For more information, visit our GitHub repository: https://github.com/leo4life2/algoml-final

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BibTeX RIS

Zezheng Li, Kingston Yip. 2024-04-12. BERT-LSH: Reducing Absolute Compute For Attention. https://arxiv.org/abs/2404.08836

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