3 citations · 4 across the 14 of their papers we have counts for
5 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…
Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods
Xinyang Hu, Fengzhuo Zhang, Siyu Chen +1
Chain-of-Thought (CoT) prompting and its variants have gained popularity as effective methods for solving multi-step reasoning problems using pretrained large language models (LLMs…
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems
Jianliang He, Siyu Chen, Fengzhuo Zhang +1
In this work, from a theoretical lens, we aim to understand why large language model (LLM) empowered agents are able to solve decision-making problems in the physical world. To thi…
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…
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…