4 papers
A Family of LLMs Liberated from Static Vocabularies
Aleph Alpha, :, Adnen Abdessaied +35
Tokenization is a central component of natural language processing in current large language models (LLMs), enabling models to convert raw text into processable units. Although lea…
SOMBRERO: Measuring and Steering Boundary Placement in End-to-End Hierarchical Sequence Models
Pit Neitemeier, Alessio Serra, Jiaze Li +3
Hierarchical sequence models replace fixed tokenization with learned segmentations that compress long byte sequences for efficient autoregressive modeling. While recent end-to-end…
Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models
Pit Neitemeier, Björn Deiseroth, Constantin Eichenberg +1
Tokenization is a fundamental step in natural language processing, breaking text into units that computational models can process. While learned subword tokenizers have become the…
u-P: The Unit-Scaled Maximal Update Parametrization
Charlie Blake, Constantin Eichenberg, Josef Dean +7
The Maximal Update Parametrization (P) aims to make the optimal hyperparameters (HPs) of a model independent of its size, allowing them to be swept using a cheap proxy model ra…