  Position encoding in transformer architecture provides supervision for
dependency modeling between elements at different positions in the sequence. We
investigate various methods to encode positional information in
transformer-based language models and propose a novel implementation named
Rotary Position Embedding(RoPE). The proposed RoPE encodes absolute positional
information with rotation matrix and naturally incorporates explicit relative
position dependency in self-attention formulation. Notably, RoPE comes with
valuable properties such as flexibility of being expand to any sequence
lengths, decaying inter-token dependency with increasing relative distances,
and capability of equipping the linear self-attention with relative position
encoding. As a result, the enhanced transformer with rotary position embedding,
or RoFormer, achieves superior performance in tasks with long texts. We release
the theoretical analysis along with some preliminary experiment results on
Chinese data. The undergoing experiment for English benchmark will soon be
updated.
