RoVE: New Attention Mechanism Improves Position Sensitivity in Large Language Models
Researchers propose RoVE (Rotary Value Embeddings), a parameter-free modification to Rotary Position Embeddings that makes value tokens position-sensitive in transformer attention mechanisms. The technique rotates values alongside keys, converting RoPE attention into attentive convolution and unifying similar operations across computer vision, robotics, and LLM architectures. Experiments on GPT-2 models show consistent improvements in few-shot learning, out-of-distribution performance, and long-context retrieval tasks.
A new preprint on arXiv describes RoVE, a modification to Rotary Position Embeddings (RoPE) that addresses a fundamental limitation: while RoPE makes attention scores position-relative, it leaves the value pathway position-blind, meaning tokens send the same message regardless of distance from the query. RoVE solves this by rotating values simultaneously with keys, requiring no additional parameters. The authors demonstrate that this transforms RoPE attention into attentive convolution and show it unifies several independent formulations across computer vision, robotics, and modern LLM architectures. Experiments training 124M and 354M parameter GPT-2 models show consistent empirical gains over standard RoPE on few-shot in-context learning, out-of-distribution perplexity, and long-context retrieval, with the most pronounced improvements on tasks requiring long-range aggregation.
What's missing
The preprint does not discuss computational overhead or inference speed comparisons with RoPE, nor does it provide details on how RoVE scales to larger models (e.g., billion-parameter scale). The paper also does not compare against other recent position-aware value mechanisms or discuss potential limitations of the approach.
What different sources said
- arXiv cs.AICenter
RoVE: Rotary Value Embeddings Attention for Relative Position-dependent Value Pathways
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