4 papers
Scaling Limits of Long-Context Transformers
Giuseppe Bruno, Shi Chen, Zhengjiang Lin +2
We study the long-context limit of softmax self-attention with a fixed query and a random context of i.i.d. keys on the sphere, viewing the inverse temperature as the sc…
Quantitative Clustering in Mean-Field Transformer Models
Shi Chen, Zhengjiang Lin, Yury Polyanskiy +1
The evolution of tokens through deep transformer models can be modeled as an interacting particle system that has been shown to exhibit an asymptotic clustering behavior akin to th…
Critical attention scaling in long-context transformers
Shi Chen, Zhengjiang Lin, Yury Polyanskiy +1
As large language models scale to longer contexts, attention layers suffer from a fundamental pathology: attention scores collapse toward uniformity as context length increases…
Residual connections provably mitigate oversmoothing in graph neural networks
Ziang Chen, Zhengjiang Lin, Shi Chen +2
Graph neural networks (GNNs) have achieved remarkable empirical success in processing and representing graph-structured data across various domains. However, a significant challeng…