Summary of Clustering Head: a Visual Case Study Of the Training Dynamics in Transformers, by Ambroise Odonnat et al.
Clustering Head: A Visual Case Study of the Training Dynamics in Transformers
by Ambroise Odonnat, Wassim Bouaziz, Vivien Cabannes
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel paper introduces the sparse modular addition task, exploring how transformers learn this challenge. The study focuses on transformers with 2D embeddings, developing a visual sandbox for in-depth layer-by-layer training process visualization. The research uncovers “clustering heads,” a type of circuit that grasps problem invariants. Analyzing these circuits’ training dynamics reveals two-stage learning, loss spikes due to high curvature or normalization layers, and the effects of initialization and curriculum learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers learn a new task called sparse modular addition. It looks at special computer models called transformers that can do this job well. The researchers created a tool that lets them see what’s happening inside these models as they’re learning. They found something interesting – certain parts of the model are good at understanding patterns in the data. They studied how these parts learn and found some surprising things, like how the learning process can go through ups and downs. |
Keywords
» Artificial intelligence » Clustering » Curriculum learning
