RoFormer: Enhanced Transformer with Rotary Position Embedding

Formulated Rotary Position Embedding (RoPE), encoding relative positional information via complex rotation matrices applied directly to query and key vectors.

Directly guides architectural decisions, alignment strategy, and serving infrastructure for task-text-generation, task-feature-extraction.

Assumptions

• Empirical distribution regularity holds and target domain adheres to pretraining linguistic/visual support

Limitations

• Resource scaling, inference memory requirements, and alignment robustness vary with model size and hardware topology