利用作者风格数据提升论文图注个性化生成效果
Personalized Scientific Figure Caption Generation: An Empirical Study on Author-Specific Writing Style Transfer
- 基于作者资料与元数据增强多模态大模型的风格迁移能力
- 作者风格匹配度提升，但图注质量出现明显下降
- 揭示风格与质量的权衡关系，指导实用系统设计
我们研究了利用科学论文中的作者资料进行个性化图注生成的方法。实验表明，结合丰富的作者档案数据和相关元数据，能显著提升多模态大语言模型的个性化表现。然而，我们也发现作者风格匹配度与图注质量之间存在根本性权衡。这些发现为开发兼顾两者目标的实用化图注自动生成系统提供了重要启示。本工作是第三届SciCap挑战赛的一部分。
原文摘要 · Abstract (English)
We study personalized figure caption generation using author profile data from scientific papers. Our experiments demonstrate that rich author profile data, combined with relevant metadata, can significantly improve the personalization performance of multimodal large language models. However, we also reveal a fundamental trade-off between matching author style and maintaining caption quality. Our findings offer valuable insights and future directions for developing practical caption automation systems that balance both objectives. This work was conducted as part of the 3rd SciCap challenge.
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