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LAWCAT: Efficient Distillation from Quadratic to Linear Attention with Convolution across Tokens for Long Context Modeling (2025.findings-emnlp)

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Challenge: a novel linearization framework is proposed to reduce the cost of training transformers from scratch.
Approach: They propose a linear attention framework that integrates pre-trained transformers into a performant linear attention architecture.
Outcome: The proposed framework improves performance on mistral-7B with 1K-length sequences and BABILong benchmarks.

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