Transformer linearization method improves long-context inference for LLaMA and Qwen models

Researchers have developed a method to linearize transformer models, addressing the quadratic cost of causal self-attention that hinders long-context inference. By isolating the effect of state update design and introducing structural interventions like sink tokens, short convolutions, and fixed-budget cache routing, they significantly reduced approximation errors. This approach, tested on LLaMA and Qwen models up to 32B parameters, achieved superior performance on MMLU benchmarks and matched complex adaptive-caching frameworks in long-context retrieval. AI

IMPACT This research could lead to more efficient and capable long-context AI models, improving performance on tasks requiring extensive context.

RANK_REASON This is a research paper detailing a new method for improving transformer model efficiency. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• LLaMA
• MMLU
• Qwen

• paper
• infra

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