Breaking the Attention Bottleneck
<|reference_start|>Breaking the Attention Bottleneck: Attention-based transformers have become the standard architecture in many deep learning fields, primarily due to their ability to model long-range dependencies and handle variable-length input sequences. However, the attention mechanism with its quadratic complexity is a significant bottleneck in the transformer architecture. This algorithm is only uni-directional in the decoder and converges to a static pattern in over-parametrized decoder-only models. I address this issue by developing a generative function as attention or activation replacement. It still has the auto-regressive character by comparing each token with the previous one. In my test setting with nanoGPT this yields a smaller loss while having a smaller model. The loss further drops by incorporating an average context vector. This concept of attention replacement is distributed under the GNU AGPL v3 license at https://gitlab.com/Bachstelze/causal_generation.<|reference_end|>
@article{hilsenbek2024breaking,
    title={Breaking the Attention Bottleneck},
    author={Kalle Hilsenbek},
    journal={arXiv preprint arXiv:2406.10906},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10906},
    primaryClass={cs.LG cs.CL}
}