通过动态协作基于路由的检索重新排名来增强时尚智能制造的大型语言模型
IEEE transactions on cybernetics
|October 30, 2025
概括
本研究介绍了一种用于时尚制造的大型语言模型 (LLM) 的新型检索优化框架. 动态囊路由网络 (SGDCR) 通过过冗余信息来改善技术支持,以实现更好的智能生产.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 信息检索 信息检索
背景情况:
- 时尚制造业的大型语言模型 (LLM) 需要外部知识来提供可靠的技术支持.
- 时装制造业的专业术语和复杂的查询挑战了现有的检索方法.
- 简单的查询匹配导致无关和冗余的文档块,阻碍了LLM的性能.
研究的目的:
- 提出一个检索优化框架,以提高时尚制造知识检索的LLM性能.
- 为解决文本上松散和冗余的文档检索问题.
- 提高LLMs提供的技术支持和决策协助的质量.
主要方法:
- 提出了一个带有嵌入式语义图 (SGDCR) 框架的动态囊路由网络.
- 该框架采用了两步过程:过和重新排名检索的文件.
- 一个囊路由机制模拟文件之间的语义关系和贡献分数以进行过,然后进行深度语义相似性匹配以重新排名.
主要成果:
- 该SGDCR框架有效地过了不相关和多余的文档块.
- 重新排名整合了相关性和贡献分数,以实现上下文连贯的文档提示.
- 实验结果显示,与QA数据集的现有重新排名方法相比,性能优越.
结论:
- 拟议的SGDCR框架显著提高了LLM的时装制造知识检索的准确性和效率.
- 这种方法可以提高工艺控制和智能生产效率.
- 该方法在一般的开放领域QA和专门的时装制造QA系统中都表现出有效性.
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