通过模块化线性化注意力提高自回归 NLP 任务的性能（英文版）

英文标题：Improving Autoregressive NLP Tasks via Modular Linearized Attention中文摘要：本文提出基于模块化线性化注意力（MLA）的自然语言处理技术，通过结合多种高效的注意力机制，并验证其在自回归任务上显著提升了推理质量和效率。英文摘要：Various natural language processing (NLP) tasks necessitate models that areefficient and small based on their ultimate application at the edge or in otherresource-constrained environments. While prior research has reduced the size ofthese models, increasing computational efficiency without considerableperformance impacts remains difficult, especia