This paper introduces RoFormer, an enhanced Transformer model that leverages Rotary Position Embedding (RoPE) to improve natural language processing tasks. The authors explore existing methods for incorporating positional information into Transformer architectures, contrasting traditional additive position encoding with their novel multiplicative approach. RoPE encodes absolute position through a rotation matrix while explicitly integrating relative position dependency within the self-attention mechanism, offering benefits such as flexibility in sequence length and decaying inter-token dependency over distance. Experimental results across machine translation, pre-training language models, and fine-tuning on GLUE benchmarks, including long text and Chinese datasets, consistently demonstrate RoFormer's superior performance and faster convergence compared to alternative models. The paper also provides a theoretical derivation and properties of RoPE, despite acknowledging some limitations in fully explaining certain empirical observations.