用图画图，测试大模型看图做图任务的表现
Graph Drawing for LLMs: An Empirical Evaluation
- 让大模型看图形布局完成任务，测试不同画法影响
- 合理布局和清晰绘图可显著提升模型表现
- 提示词设计是关键，需针对性优化
本研究关注大语言模型（LLMs）在图相关任务中的应用，尤其聚焦于依赖视觉模态的场景——将待分析的图以图像形式输入模型。我们探讨了布局方式、图形美学质量以及提示策略对模型性能的影响，并提出三个研究问题，进行了系统的实验分析。结果表明，选择合适的布局范式并从人类可读性角度优化输入图形，能显著提升模型在任务上的表现；同时，选取最有效的提示技术是实现最优性能的关键挑战。
原文摘要 · Abstract (English)
Our work contributes to the fast-growing literature on the use of Large Language Models (LLMs) to perform graph-related tasks. In particular, we focus on usage scenarios that rely on the visual modality, feeding the model with a drawing of the graph under analysis. We investigate how the model's performance is affected by the chosen layout paradigm, the aesthetics of the drawing, and the prompting technique used for the queries. We formulate three corresponding research questions and present the results of a thorough experimental analysis. Our findings reveal that choosing the right layout paradigm and optimizing the readability of the input drawing from a human perspective can significantly improve the performance of the model on the given task. Moreover, selecting the most effective prompting technique is a challenging yet crucial task for achieving optimal performance.
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