arXiv Machine Learning
Jun 25

Emergent Capabilities Arise Randomly from Learning Sparse Attention Patterns

arXiv:2606. 25010v1 Announce Type: new Abstract: Neural scaling laws for transformer language models predict smooth improvements in pretraining loss with increasing parameters, but downstream capabilities such as in-context learning are known to emerge abruptly past a certain model scale.

By Vatsal Baherwani, Zixi Chen, Shikai Qiu, Andrew Gordon Wilson, Pavel Izmailov
arXiv Machine Learning
Jul 7

Incremental Learning of Sparse Attention Patterns in Transformers

arXiv:2602. 19143v2 Announce Type: replace Abstract: This paper studies simple transformers trained on a high-order Markov chain, where the model must incorporate information from multiple past positions, each with different statistical importance.

By O\u{g}uz Kaan Y\"uksel, Rodrigo Alvarez Lucendo, Nicolas Flammarion