arXiv:2410.24050v3 Announce Type: replace Abstract: Large-scale pretraining of transformers has been central to the success of foundation models. However, the scale of those models limits our understanding of the mechanisms at play during optimization. In this work, we study the training dynamics of transformers in a controlled and interpretable setting. On the sparse modular addition task, we demonstrate that specialized attention circuits, called clustering heads, can be implemented during gradient descent to solve the problem. Our experiments show that such pathways naturally emerge during
This research provides deeper insight into the internal workings of transformers, which is critical as their scale and deployment continue to expand rapidly.
Understanding the mechanistic details of transformer training is crucial for developing more efficient, reliable, and interpretable AI models, addressing some of the 'black box' criticisms.
This research offers potential pathways for designing more optimized and robust transformer architectures, moving beyond brute-force scaling to more principled development.
- · AI researchers
- · ML engineers
- · Foundation model developers
- · AI ethics and safety organizations
- · Developers relying solely on trial-and-error scaling
- · Researchers without access to large-scale computational resources
Improved understanding of transformer architecture and training dynamics.
Development of next-generation transformer models that are more interpretable and efficient.
Acceleration of AI capabilities due to more principled model design, potentially impacting a wide array of applications from healthcare to defense.
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Read at arXiv cs.LG