使用大型语言模型进行交互式因果模型开发和改进
IEEE transactions on visualization and computer graphics
|August 25, 2025
概括
这项研究介绍了CausalChat,一种使用大型语言模型 (LLM) 构建因果网络的视觉分析工具. 用户可以通过对话互动探索变量关系并识别因果结构.
科学领域:
- 数据科学
- 人工智能
- 网络科学
背景情况:
- 因果网络对于模拟不同领域变量之间的复杂关系至关重要.
- 现有的因果网络构建方法通常依赖于人类的专业知识,需要大量的领域知识和参与.
研究的目的:
- 通过利用大型语言模型 (LLM) 中嵌入的知识来构建因果网络的新方法.
- 介绍一个视觉分析界面,用于交互式因果网络发现.
- 用不同的数据集和用户群体评估CausalChat的有效性.
主要方法:
- 利用LLM (例如GPT-4) 从广泛的文献中获得的因果知识.
- 开发了一个视觉分析界面 (CausalChat),使变量可被递归探索.
- 将用户交互转化为定制的LLM提示,用于识别因果关系,潜在变量,混因素和调解因素.
- 集成的视觉表示与文本解释,以提高理解.
主要成果:
- 在各种数据环境中展示了CausalChat的功能.
- 涉及领域专家和非专业人士的用户研究证实了该工具的有用性.
- 通过对话式探索,该系统成功地建立了详细的因果网络.
结论:
- 提供创新的因果网络构建方法,减少对广泛的人类领域专业知识的依赖.
- 基于LLM的视觉分析为复杂的数据关系发现提供了一个有前途的途径.
- 对于具有不同领域知识水平的用户来说,该方法具有适应性和有效性.
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