---
id: 20260411-T0-10
title: "SepSeq：无需训练即可处理长数字序列的LLM框架"
title_en: "SepSeq: Training-Free LLM Framework for Long Number Sequences"
url: https://ai.daily.yangsir.net/daily/20260411-T0-10
issue_date: 2026-04-11
publish_date: 2026-04-10T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2604.07737
---

# SepSeq：无需训练即可处理长数字序列的LLM框架

研究团队发布SepSeq框架，解决Transformer模型处理长数字序列时的性能下降问题。该方案通过改进注意力机制分散问题，在无需额外训练的情况下，将LLM处理数字序列的准确率提升50%。测试显示，在金融数据处理和科学计算等场景中效果显著。开发者可直接将SepSeq集成到现有模型中，提升对长数字序列的理解和生成能力。

## English Version

**SepSeq: Training-Free LLM Framework for Long Number Sequences**

Researchers released SepSeq, addressing Transformer's performance degradation on long number sequences. By improving attention dispersion, it boosts accuracy by 50% without additional training. Effective for financial data and scientific computing, developers can integrate it directly into existing models.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2604.07737)

**详情页**：https://ai.daily.yangsir.net/daily/20260411-T0-10

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