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arXiv · 2407.20570

Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education

Lin Gao·Jing Lu·Zekai Shao·Ziyue Lin·Shengbin Yue·Chiokit Ieong·Yi Sun·Rory James Zauner·Zhongyu Wei·Siming Chen
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Abstract

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.

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Lin Gao, Jing Lu, Zekai Shao, Ziyue Lin, Shengbin Yue, Chiokit Ieong, Yi Sun, Rory James Zauner, Zhongyu Wei, Siming Chen. 2024-07-30. Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education. https://arxiv.org/abs/2407.20570

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