Attention Is All You Need: A Retrospective

Your Name^*, Co-Author One^*, Co-Author Two ^* equal contribution

NeurIPS 2024 · Workshop on Retrospectives in ML

Abstract

We revisit the original Transformer architecture seven years on, examining what the attention mechanism actually learned and why certain design choices proved surprisingly durable. This is a placeholder abstract — replace it with your real one.

Contribution summary

This section is where you write an extended, blog-style explanation of your paper for a general technical audience. Explain the key idea without assuming the reader has read the PDF.

Key results

Describe your most important results here. Use tables, figures, and math as needed. KaTeX is enabled for this page (math = true).

For example, the core attention operation is:

$$\text{Attention}(Q, K, V) = \text{softmax}!\left(\frac{QK^\top}{\sqrt{d_k}}\right) V$$

Reproducibility notes

Point readers to your code, datasets, and any gotchas in reproducing the results.

BibTeX

@inproceedings{yourname2024attention,
  title     = {Attention Is All You Need: A Retrospective},
  author    = {Your Name and Co-Author One and Co-Author Two},
  booktitle = {NeurIPS Workshop on Retrospectives in ML},
  year      = {2024},
  url       = {https://arxiv.org/abs/PLACEHOLDER}
}