故事带:用大型语言模型重新想象故事情节可视化
IEEE transactions on visualization and computer graphics
|December 4, 2025
概括
大型语言模型 (LLM) 自动化叙事数据提取用于文学分析. 这使得可以创建交互式可视化,如故事带,揭示了新见解的人物和主题轨迹.
科学领域:
- 数字人文学科 数字人文学科
- 计算语言学 计算语言学
- 文学研究 文学研究
背景情况:
- 分析文学文本涉及复杂的关系映射 (角色,主题).
- 从非结构化叙述中提取结构化数据是一个重大挑战.
- 大型语言模型 (LLM) 的进步为文本分析提供了新的可能性.
研究的目的:
- 为了利用LLMs进行自动叙事信息提取.
- 为文学分析开发一个交互式可视化系统.
- 探索LLM在增强故事情节可视化技术方面的能力.
主要方法:
- 为小说和剧本开发了一个LLM驱动的数据解析管道.
- 创建了一个交互式可视化系统 - - STORY RIBBONS.
- 对36个文学作品进行了评估和用户研究.
主要成果:
- 证明了LLM在简化叙事可视化创作方面的潜力.
- 展示了 STORY RIBBONS 揭示人物和主题轨迹的洞察力的能力.
- 在文献分析中确定了基于人工智能的系统的当前局限性.
结论:
- 法律学士学位可以显著提高文学可视化过程.
- 交互式系统,如故事带,促进了对叙事结构的更深入的探索.
- 需要进一步的研究来解决人工智能的局限性,并完善交互动机.
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