Summary of Denseformer: Enhancing Information Flow in Transformers Via Depth Weighted Averaging, by Matteo Pagliardini et al.
DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging
by Matteo Pagliardini, Amirkeivan Mohtashami, Francois Fleuret, Martin Jaggi
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a modification to the widely used Transformer architecture by Vaswani et al. (2017), called DenseFormer. This new approach adds an additional averaging step after each Transformer block, referred to as Depth-Weighted-Average (DWA). The DWA weights exhibit coherent patterns of information flow, showing the reuse of activations from distant layers. Experiments demonstrate that DenseFormer is more data-efficient and achieves the same perplexity as much deeper transformer models. Additionally, DenseFormer outperforms Transformer baselines in terms of memory efficiency and inference time for the same perplexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a new way to make transformers work better. It’s called DenseFormer and it adds a simple trick to the original idea by Vaswani et al. (2017). This trick makes the model use information from earlier layers more efficiently, which is good because it can help reduce the amount of data needed to train the model. The new method also works faster and uses less memory than older transformer models. Overall, DenseFormer is a way to make transformers work better without making them too complicated. |
Keywords
* Artificial intelligence * Inference * Perplexity * Transformer
