大型语言模型的视觉素养有多高? 关于近期进展和未来方向的思考
IEEE computer graphics and applications
|November 11, 2025
概括
大型语言模型 (LLM) 在创建和解释数据可视化方面表现有前途,但与人类相比,在可用性和分析任务方面面临挑战.
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:
- 大型语言模型 (LLM) 越来越多地用于可视化任务.
- 这些模型在生成和解释方面都显示出"视觉素养"的潜力.
研究的目的:
- 审查目前的LLM可视化素养的现状.
- 探索数据可视化LLM应用的未来方向.
主要方法:
- 分析LLM在可视化生成方面的能力 (例如,用于创作的自然语言接口).
- 评估LLM在可视化解释方面的表现 (例如,回答问题,合成信息,检测误导性设计).
主要成果:
- 虽然LLM显示出通过自然语言生成可视化的潜力,但存在可用性问题.
- 模型可以执行基本的解释任务,但难以处理复杂的分析和类似人类的理解.
- 在LLM和人类对可视化的解释之间存在差异.
结论:
- 法学士课程在可视化生成和解释方面取得了进展.
- 需要进一步的研究,以解决分析任务和与人类一致的理解方面的局限性.
- 未来的工作应该专注于提高LLM可视化素养,以获得更强大的应用程序.
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