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October 9, 2025Open Access

Context-aware Rotary Position Embedding

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Experimental analysis shows CARoPE reduces perplexity in next-token prediction tasks with GPT-2, suggesting improved efficiency.

CARoPE outperforms traditional rotary positional embeddings and common baselines in perplexity.
The model achieves faster training throughput while maintaining stability across various context lengths.
Dynamic generation of frequency patterns based on token embeddings enhances context-sensitive positional representation.
Evaluation on the FineWeb-Edu-10B dataset highlights CARoPE's scalability and efficiency.

Veisi et al. (2025) studied this question.

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October 9, 2025Open Access

View Full Paper

Ask AI

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Authors

Experimental analysis shows CARoPE reduces perplexity in next-token prediction tasks with GPT-2, suggesting improved efficiency.

CARoPE outperforms traditional rotary positional embeddings and common baselines in perplexity.
The model achieves faster training throughput while maintaining stability across various context lengths.
Dynamic generation of frequency patterns based on token embeddings enhances context-sensitive positional representation.
Evaluation on the FineWeb-Edu-10B dataset highlights CARoPE's scalability and efficiency.

Veisi et al. (2025) studied this question.

View Full Paper

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Bookmark