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RoPE: Achieving Long-Context Understanding in LLMs
- The paper titled “RoFormer: Enhanced Transformer with Rotary Position Embedding” introduces a novel approach to positional encoding in transformer architectures through a method called Rotary Position Embedding (RoPE).
- The authors propose that the inner product of query qₘ and key kₙ be formulated by a function g, which takes only the word embeddings xₘ, xₙ, and their relative position m − n as input variables.
- They express this goal as: f(θᵤ − θₖ) ≈
- The complete math proof to arrive at this result could be done in another article.
- Rotations can be combined by adding their angles, following this rule: R(θᵢ)R(θⱼ) = R(θᵢ + θⱼ)
- This is where the relative position emerges! The matrix now represents a rotation by the difference in positions θⱼ- θᵢ, which directly encodes the relative position between tokens.
- RoFormer paper and the RoPE method proposed in it represent an advancement in transformer architecture by effectively leveraging positional information through rotary embeddings.
- This not only improves model performance but also addresses key limitations associated with traditional positional encodings, particularly in leveraging relative positions, handling long sequences and maintaining computational efficiency.
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