基于大型语言模型的智能驾驶的知识图构建
Haomin Dong1,2, Wenbin Wang2, Zhenjiang Sun2
1School of Mechanical and Aerospace Engineering, Jilin University, Changchun, 130025, China.
Scientific reports
|March 4, 2025
概括
这项研究介绍了GLM-TripleGen,这是一种用于智能驾驶的新型知识图构建模型. 它准确地捕捉用户行为并增强系统理解,克服传统方法的局限性.
科学领域:
- 人与计算机的交互
- 人工智能的人工智能
- 知识表示 知识表示
背景情况:
- 智能驾驶需要先进的用户行为推断来进行主动交互.
- 传统方法面临可扩展性,概括性和准确性问题,导致伪需求函数.
- 知识图 (KG) 为组织复杂信息和了解用户需求提供了解决方案.
研究的目的:
- 开发一种新的知识图形构建 (KGC) 模型,GLM-TripleGen,用于分析智能驾驶状态和行为.
- 精确地挖掘驾驶状态和用户行为之间的潜在关系.
- 为应对驾驶数据中的实体识别和关系提取方面的挑战.
主要方法:
- 推出了GLM-TripleGen,这是一个针对智能驾驶的域特定KGC模型.
- 构建了一个根据车辆状态和驾驶互动量身定制的指令遵循数据集.
- 采用低级调整 (LoRA) 方法进行高效的模型微调和参数优化.
主要成果:
- 与最先进的KGC方法相比,GLM-TripleGen表现出更高的性能.
- 该模型准确地生成了正常化的驾驶三重单元.
- 实现了强大的性能和强大的概括能力与未知的实体和关系.
结论:
- 在智能驾驶领域,GLM-TripleGen有效地解决了KGC的挑战.
- 该模型增强了系统理解用户行为和上下文信息的能力.
- 在不断发展的智能系统中,GLM-TripleGen为知识图构建提供了强大而适应性的解决方案.
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