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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2504.16358 |
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| _version_ | 1866909589005402112 |
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| author | Bai, Tian Ying, Huiyan Suo, Kailong Wei, Junqiu Fan, Tao Song, Yuanfeng |
| author_facet | Bai, Tian Ying, Huiyan Suo, Kailong Wei, Junqiu Fan, Tao Song, Yuanfeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16358 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Text-to-TrajVis: Enabling Trajectory Data Visualizations from Natural Language Questions Bai, Tian Ying, Huiyan Suo, Kailong Wei, Junqiu Fan, Tao Song, Yuanfeng Computation and Language 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. |
| title | Text-to-TrajVis: Enabling Trajectory Data Visualizations from Natural Language Questions |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2504.16358 |