BERT-LSH: Reducing Absolute Compute For Attention
<|reference_start|>BERT-LSH: Reducing Absolute Compute For Attention: 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<|reference_end|>
@article{li2024bert-lsh:,
    title={BERT-LSH: Reducing Absolute Compute For Attention},
    author={Zezheng Li, Kingston Yip},
    journal={arXiv preprint arXiv:2404.08836},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08836},
    primaryClass={cs.CL cs.AI cs.LG}
}