  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
