Large Language Models (LLMs) have shown great potential in intelligent visualization systems, especially for domain-specific applications. Integrating LLMs into visualization systems presents challenges, and we categorize these challenges into three alignments: domain problems with LLMs, visualization with LLMs, and interaction with LLMs. To achieve these alignments, we propose a framework and outline a workflow to guide the application of fine-tuned LLMs to enhance visual interactions for domain-specific tasks. These alignment challenges are critical in education because of the need for an intelligent visualization system to support beginners' self-regulated learning. Therefore, we apply the framework to education and introduce Tailor-Mind, an interactive visualization system designed to facilitate self-regulated learning for artificial intelligence beginners. Drawing on insights from a preliminary study, we identify self-regulated learning tasks and fine-tuning objectives to guide visualization design and tuning data construction. Our focus on aligning visualization with fine-tuned LLM makes Tailor-Mind more like a personalized tutor. Tailor-Mind also supports interactive recommendations to help beginners better achieve their learning goals. Model performance evaluations and user studies confirm that Tailor-Mind improves the self-regulated learning experience, effectively validating the proposed framework.

翻译：大语言模型（LLMs）在智能可视化系统中展现出巨大潜力，尤其在领域特定应用中。将LLMs集成到可视化系统面临诸多挑战，我们将这些挑战归纳为三个层面的对齐：领域问题与LLMs的对齐、可视化与LLMs的对齐、交互与LLMs的对齐。为实现这些对齐，我们提出了一个框架并概述了工作流程，以指导应用微调LLMs来增强领域特定任务的视觉交互。这些对齐挑战在教育领域尤为重要，因为智能可视化系统需要支持初学者的自我调节学习。为此，我们将该框架应用于教育领域，推出了Tailor-Mind——一个专为人工智能初学者设计的交互式可视化系统，旨在促进其自我调节学习。基于初步研究的见解，我们确定了自我调节学习任务和微调目标，用以指导可视化设计和调优数据构建。我们专注于将可视化与微调LLM对齐，使Tailor-Mind更贴近个性化导师的角色。该系统还支持交互式推荐，帮助初学者更好地达成学习目标。模型性能评估和用户研究证实，Tailor-Mind有效提升了自我调节学习体验，从而验证了所提框架的有效性。