让自然语言直接生成轨迹可视化，打通人机交互新路径。
Text-to-TrajVis: Enabling Trajectory Data Visualizations from Natural Language Questions
- 提出轨迹可视化语言TVL，统一描述轨迹查询与图形生成
- 构建首个大规模数据集TrajVL，含18,140组问答对
- 验证大模型在该任务上仍有提升空间，适合人机交互研究者
本文提出文本到轨迹可视化（Text-to-TrajVis）新任务，旨在将自然语言问题转化为轨迹数据可视化结果，推动轨迹可视化系统的自然语言接口发展。由于该任务尚无公开数据集，我们首先设计了轨迹可视化语言（TVL），用于规范轨迹查询与可视化生成。随后，结合大语言模型（LLMs）与人工标注，系统性构建高质量数据：先生成TVL，再由LLMs映射至自然语言问题。最终形成首个大规模数据集TrajVL，包含18,140个（问题, TVL）配对。基于此，我们评估了GPT、Qwen、Llama等多个大模型的性能，结果表明该任务兼具可行性与挑战性，值得学术界深入探索。
原文摘要 · Abstract (English)
This paper introduces the Text-to-TrajVis task, which aims to transform natural language questions into trajectory data visualizations, facilitating the development of natural language interfaces for trajectory visualization systems. As this is a novel task, there is currently no relevant dataset available in the community. To address this gap, we first devised a new visualization language called Trajectory Visualization Language (TVL) to facilitate querying trajectory data and generating visualizations. Building on this foundation, we further proposed a dataset construction method that integrates Large Language Models (LLMs) with human efforts to create high-quality data. Specifically, we first generate TVLs using a comprehensive and systematic process, and then label each TVL with corresponding natural language questions using LLMs. This process results in the creation of the first large-scale Text-to-TrajVis dataset, named TrajVL, which contains 18,140 (question, TVL) pairs. Based on this dataset, we systematically evaluated the performance of multiple LLMs (GPT, Qwen, Llama, etc.) on this task. The experimental results demonstrate that this task is both feasible and highly challenging and merits further exploration within the research community.
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