用大模型编码轨迹，能保留部分距离关系，但恢复数值和找邻近点仍有困难。
Evaluating the Effectiveness of Large Language Models in Representing and Understanding Movement Trajectories
- 用GPT-J将轨迹转为字符串并生成嵌入向量
- 嵌入向量与原始轨迹的距离相关性超0.74（皮尔逊相关）
- 适合做时空依赖分析和位置预测，但不擅长精准还原地理数据
本研究评估大语言模型在表征运动轨迹方面的表现。我们采用GPT-J对轨迹的字符串格式进行编码，并评估其嵌入表示在轨迹数据分析中的有效性。实验表明，尽管基于LLM的嵌入能保持部分轨迹距离度量（例如，从GPT-J嵌入计算的余弦距离与原始轨迹的豪斯多夫距离和动态时间规整距离之间的相关系数超过0.74），但在恢复数值信息和检索空间邻居方面仍存在挑战。此外，大语言模型能够理解轨迹中蕴含的时空依赖关系，在位置预测任务中表现出良好准确性。该研究强调了当前模型在捕捉地理空间数据细微特征及整合领域知识方面的不足，亟需改进以支持各类GeoAI应用。
原文摘要 · Abstract (English)
This research focuses on assessing the ability of AI foundation models in representing the trajectories of movements. We utilize one of the large language models (LLMs) (i.e., GPT-J) to encode the string format of trajectories and then evaluate the effectiveness of the LLM-based representation for trajectory data analysis. The experiments demonstrate that while the LLM-based embeddings can preserve certain trajectory distance metrics (i.e., the correlation coefficients exceed 0.74 between the Cosine distance derived from GPT-J embeddings and the Hausdorff and Dynamic Time Warping distances on raw trajectories), challenges remain in restoring numeric values and retrieving spatial neighbors in movement trajectory analytics. In addition, the LLMs can understand the spatiotemporal dependency contained in trajectories and have good accuracy in location prediction tasks. This research highlights the need for improvement in terms of capturing the nuances and complexities of the underlying geospatial data and integrating domain knowledge to support various GeoAI applications using LLMs.
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