法律学士是否具备可视化素养? 对修改的可视化进行评估,以测试数据解释中的泛化
IEEE transactions on visualization and computer graphics
|March 3, 2025
概括
像GPT-4和双子座这样的大型语言模型 (LLM) 显示出有限的可视化素养,表现低于人类水平. 这些人工智能模型依赖于事先存在的知识,而不是解释视觉数据.
科学领域:
- 人工智能的人工智能
- 数据可视化 数据可视化
- 人与计算机的交互
背景情况:
- 大型语言模型 (LLM) 显示出在生成图表描述和设计建议方面的潜力.
- 对于LLM评估数据可视化的能力,仍然在很大程度上未被探索.
- 用于可视化评估的人类数据收集是耗时且昂贵的,这凸显了对自动化解决方案的需求.
研究的目的:
- 评估著名的LLM的可视化素养,特别是OpenAI的GPT-4和谷歌的Gemini.
- 为评估LLM在理解和解释数据可视化方面的能力建立基准.
- 调查在可视化研究过程中使用LLM作为评估者的可行性.
主要方法:
- 对GPT-4和Gemini进行了修改后的53项可视化识字测试 (VLAT).
- 分析了LLM的回应,以衡量他们对视觉数据的理解.
- 性能与从VLAT获得的现有人类数据进行了比较.
主要成果:
- 与一般公众相比,GPT-4和双子座的可视化素养都较低.
- 士表现出倾向于依赖他们事先存在的知识基础.
- 模型努力解释和利用直接呈现在可视化中的信息.
结论:
- 目前的LLM缺乏必要的可视化素养,以便在可视化研究中有效地作为评估者使用.
- 需要进一步发展,以提高LLM准确解释视觉数据的能力.
- 在数据可视化评估中,LLM依赖于先前知识而不是视觉证据,这限制了它们在数据可视化评估中的有用性.
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