Linear Transformers with Learnable Kernel Functions are Better
  In-Context Models
Yaroslav Aksenov Nikita Balagansky Sofia Maria Lo Cicero Vaina Boris Shaposhnikov Alexey Gorbatovski Daniil Gavrilov
Advancing the frontier of subquadratic architectures for Language Models
(LMs) is crucial in the rapidly evolving field of natural language processing.
Current innovations, including State Space Models, were initially celebrated
for surpassing Transformer performance on language modeling tasks. However,
these models have revealed deficiencies in essential In-Context Learning
capabilities - a domain where the Transformer traditionally shines. The Based
model emerged as a hybrid solution, blending a Linear Transformer with a kernel
inspired by the Taylor expansion of exponential functions, augmented by
convolutional networks. Mirroring the Transformer's in-context adeptness, it
became a strong contender in the field. In our work, we present a singular,
elegant alteration to the Based kernel that amplifies its In-Context Learning
abilities evaluated with the Multi-Query Associative Recall task and overall
language modeling process, as demonstrated on the Pile dataset.