---
id: 20260314-T0-17
title: "ARACH论文：通过全局注意力重分配提升LLM"
title_en: "ARACH Paper: Enhancing LLMs via Global Attention Reallocation"
url: https://ai.daily.yangsir.net/daily/20260314-T0-17
issue_date: 2026-03-14
publish_date: 2026-03-13T04:00:00.000Z
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2603.11067
---

# ARACH论文：通过全局注意力重分配提升LLM

arXiv论文《ARACH：说话前先总结》提出了一种无需训练的推理时插件方法，通过全局注意力重分配提升大型语言模型性能。该方法在推理时优化模型注意力机制，避免昂贵的再训练过程，有效提高模型输出质量。

## English Version

**ARACH Paper: Enhancing LLMs via Global Attention Reallocation**

The arXiv paper 'Summarize Before You Speak with ARACH' presents a training-free inference-time method that enhances LLMs via global attention reallocation. It optimizes attention mechanisms at inference time without costly retraining, improving output quality.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2603.11067)

**详情页**：https://ai.daily.yangsir.net/daily/20260314-T0-17

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