8 citations · 39 across the 19 of their papers we have counts for
4 papers · 1 filter
Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers
Siyu Chen, Heejune Sheen, Tianhao Wang +1
In-context learning (ICL) is a cornerstone of large language model (LLM) functionality, yet its theoretical foundations remain elusive due to the complexity of transformer architec…
Implicit Regularization of Gradient Flow on One-Layer Softmax Attention
Heejune Sheen, Siyu Chen, Tianhao Wang +1
We study gradient flow on the exponential loss for a classification problem with a one-layer softmax attention model, where the key and query weight matrices are trained separately…
How Well Can Transformers Emulate In-context Newton's Method?
Angeliki Giannou, Liu Yang, Tianhao Wang +2
Transformer-based models have demonstrated remarkable in-context learning capabilities, prompting extensive research into its underlying mechanisms. Recent studies have suggested t…
Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality
Siyu Chen, Heejune Sheen, Tianhao Wang +1
We study the dynamics of gradient flow for training a multi-head softmax attention model for in-context learning of multi-task linear regression. We establish the global convergenc…