---
id: 20260310-T0-13
title: "注意力机制结合可达性优化语法解码"
title_en: "Attention + Accessibility Optimizes Grammar Decoding"
url: https://ai.daily.yangsir.net/daily/20260310-T0-13
issue_date: 2026-03-10
publish_date: 2026-03-09T04:00:00.000Z
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2603.05540
---

# 注意力机制结合可达性优化语法解码

arXiv研究证明语法约束解码（GCD）在自回归模型与下推系统可达性查询间存在不变性定理。该技术将语言等价性分解为结构等价性，使语法解码效率提升30%，尤其在自然语言生成任务中减少重复输出。对比传统解码方法，新方案在保持语法正确的同时，降低了计算复杂度。

## English Version

**Attention + Accessibility Optimizes Grammar Decoding**

arXiv research proves an invariance theorem between grammar-constrained decoding (GCD) in autoregressive models and pushdown system reachability queries. The tech decomposes linguistic equivalence into structural equivalence, boosting grammar decoding efficiency by 30%, reducing repetitive output in NLG. The new scheme lowers computational complexity while maintaining grammatical correctness.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2603.05540)

**详情页**：https://ai.daily.yangsir.net/daily/20260310-T0-13

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