KV Shifting Attention Enhances Language Modeling
Authors: name: Mingyu Xu, name: Wei Cheng, name: Bingning Wang, name: Weipeng Chen

The current large language models are mainly based on decode-only structure
transformers, which have great in-context learning (ICL) capabilities. It is
generally believed that the important foundation of its ICL capability is the
induction heads mechanism, which requires at least two layers attention. In
order to more efficiently implement the ability of the model's induction, we
revisit the induction heads mechanism and proposed a KV shifting attention. We
theoretically prove that the KV shifting attention reducing the model's
requirements for the depth and width of the induction heads mechanism. Our
experimental results demonstrate that KV shifting attention is beneficial to
learning induction heads and language modeling, which lead to better
performance or faster convergence from toy models to the pre-training models
with more than 10 B parameters.