However, these models suffer from quadratic computational cost in the input sequence lengthn to compute pairwise attention in each layer.

Attention-based transformers have been remarkably successful at modeling generative processes across various domains and modalities.

Large language models (LLMs) have demonstrated remarkable capabilities in understanding and generating natural language data.

We also present some additional ablation studies.

Due to their size, the capability of these networks has increased tremendously, but this has come at the cost of a significant increase in necessary compute.

Transformers may have sufficient supervision for generalization.

The rapid scaling of language models is motivating research using low-bitwidth quantization. In this work, we propose a novel binarization technique for Transformers applied to machine translation (BMT), the first of its kind.

In particular, most existing work only theoretically explains how the attention mechanism facilitates ICL under certain data models.

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