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English(EN) LEBGen: An LLM-Enhanced Bayesian Network Framework for Few-Shot Travel Survey Data Generation

增强型大语言模型的贝叶斯网络改进旅行调查数据生成

研究人员开发了LEBGen,一个将大语言模型(LLMs)增强贝叶斯网络的新框架,以改进从有限样本生成旅行调查数据。该方法利用LLM生成的行为知识来优化贝叶斯网络结构,解决了在稀疏数据集中捕获复杂依赖关系的局限性。在香港旅行特征调查的2%少样本实验中,LEBGen与现有方法相比显著降低了分布和依赖性误差。 AI

影响 该框架通过改进从有限调查样本生成合成数据,有望实现更高效、更准确的旅行行为分析。

排序理由 该集群描述了一篇详细介绍新数据生成框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

增强型大语言模型的贝叶斯网络改进旅行调查数据生成

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该集群描述了一篇详细介绍新数据生成框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zijian Shen, Bin Zhou, Jiguang Wang, Ya Zhao, Jintao Ke ·

    LEBGen:一种用于少样本旅行调查数据生成的 LLM 增强贝叶斯网络框架

    arXiv:2609.08288v1 Announce Type: new Abstract: Travel survey data are essential for transportation planning and travel behavior analysis, yet collecting large-scale representative samples is costly and time-consuming. A practical alternative is to generate synthetic survey recor…