VisEval:在大型语言模型时代,数据可视化的一个基准
IEEE transactions on visualization and computer graphics
|September 10, 2024
概括
我们介绍 VisEval,这是一个用于评估自然语言到可视化 (NL2VIS) 生成的大型语言模型 (LLM) 的新基准. VisEval包括一个大数据集和自动化评估方法来评估LLM在创建准确和可读可读的可视化中的表现.
科学领域:
- 计算机科学 计算机科学
- 数据可视化 数据可视化
- 人工智能的人工智能
背景情况:
- 自然语言对可视化 (NL2VIS) 对于视觉数据分析至关重要,但在技术上要求很高.
- 大型语言模型 (LLM) 显示了NL2VIS的潜力,但缺乏标准化的评估基准.
- 现有的NL2VIS方法需要在自然语言处理和可视化设计中进行复杂的低级实施.
研究的目的:
- 解决在NL2VIS任务中评估LLM的综合基准的需要.
- 引入 VisEval,一个包含大规模数据集和自动化评估方法的新基准.
- 提供可靠的洞察力,了解当前LLM用于可视化生成的功能和局限性.
主要方法:
- 开发了一个高质量的,大规模的数据集,在146个数据库和基本真相标签中进行了2,524个查询.
- 倡导全面的自动化评估方法,评估生成的可视化图像的有效性,合法性和可读性.
- 使用异质检查器系统检测问题,以确保可靠的评估结果.
主要成果:
- 该Viseval基准被应用于几个最先进的LLMs.
- 评估揭示了当前LLM能够从自然语言中生成准确和有效的可视化能力的重大挑战.
- 该研究确定了基于LLM的NL2VIS系统需要改进的关键领域.
结论:
- VisEval提供了一个强大的框架,用于对LLMs的NL2VIS能力进行基准测试.
- 这些发现突出了进一步研究和开发的必要性,以提高在可视化生成中LLM的性能.
- 这项工作为推进自动化视觉数据分析领域提供了必要的见解.
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