Summary of Counting in Small Transformers: the Delicate Interplay Between Attention and Feed-forward Layers, by Freya Behrens et al.
Counting in Small Transformers: The Delicate Interplay between Attention and Feed-Forward Layers
by Freya Behrens, Luca Biggio, Lenka Zdeborová
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 investigates how different architectural design choices influence the space of solutions that a transformer can implement and learn. By characterizing the solutions simple transformer blocks can implement when solving the histogram task, the study reveals a rich phenomenology, highlighting the inter-dependence between model performance and hyperparameters like vocabulary and embedding sizes, token-mixing mechanisms, and feed-forward block capacity. The paper identifies two counting strategies that small transformers can implement theoretically: relation-based and inventory-based counting, with the latter being less efficient in computation and memory. The emergence of these strategies is influenced by subtle synergies among hyperparameters and components, depending on seemingly minor architectural tweaks like the inclusion of softmax in the attention mechanism. The study verifies the formation of both mechanisms in practice through introspecting models trained on the histogram task, demonstrating that slight variations in model design can cause significant changes to the solutions a transformer learns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how different design choices affect what transformers can learn and do. It looks at simple tasks like counting items in a list and finds that tiny changes in the architecture of the model can make big differences in its performance. The study shows two ways that small transformers can count, one being more efficient than the other. The results highlight how careful adjustments to the design of the model can lead to different solutions. |
Keywords
* Artificial intelligence * Attention * Embedding * Softmax * Token * Transformer
