A Dataset for Spatiotemporal-Sensitive POI Question Answering
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918022122307584 |
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| author | Han, Xiao Pan, Dayan Zhao, Xiangyu Hu, Xuyuan Deng, Zhaolin Kong, Xiangjie Shen, Guojiang |
| author_facet | Han, Xiao Pan, Dayan Zhao, Xiangyu Hu, Xuyuan Deng, Zhaolin Kong, Xiangjie Shen, Guojiang |
| contents | Spatiotemporal relationships are critical in data science, as many prediction and reasoning tasks require analysis across both spatial and temporal dimensions--for instance, navigating an unfamiliar city involves planning itineraries that sequence locations and timing cultural experiences. However, existing Question-Answering (QA) datasets lack sufficient spatiotemporal-sensitive questions, making them inadequate benchmarks for evaluating models' spatiotemporal reasoning capabilities. To address this gap, we introduce POI-QA, a novel spatiotemporal-sensitive QA dataset centered on Point of Interest (POI), constructed through three key steps: mining and aligning open-source vehicle trajectory data from GAIA with high-precision geographic POI data, rigorous manual validation of noisy spatiotemporal facts, and generating bilingual (Chinese/English) QA pairs that reflect human-understandable spatiotemporal reasoning tasks. Our dataset challenges models to parse complex spatiotemporal dependencies, and evaluations of state-of-the-art multilingual LLMs (e.g., Qwen2.5-7B, Llama3.1-8B) reveal stark limitations: even the top-performing model (Qwen2.5-7B fine-tuned with RAG+LoRA) achieves a top 10 Hit Ratio (HR@10) of only 0.41 on the easiest task, far below human performance at 0.56. This underscores persistent weaknesses in LLMs' ability to perform consistent spatiotemporal reasoning, while highlighting POI-QA as a robust benchmark to advance algorithms sensitive to spatiotemporal dynamics. The dataset is publicly available at https://www.kaggle.com/ds/7394666. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_10928 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Dataset for Spatiotemporal-Sensitive POI Question Answering Han, Xiao Pan, Dayan Zhao, Xiangyu Hu, Xuyuan Deng, Zhaolin Kong, Xiangjie Shen, Guojiang Machine Learning Spatiotemporal relationships are critical in data science, as many prediction and reasoning tasks require analysis across both spatial and temporal dimensions--for instance, navigating an unfamiliar city involves planning itineraries that sequence locations and timing cultural experiences. However, existing Question-Answering (QA) datasets lack sufficient spatiotemporal-sensitive questions, making them inadequate benchmarks for evaluating models' spatiotemporal reasoning capabilities. To address this gap, we introduce POI-QA, a novel spatiotemporal-sensitive QA dataset centered on Point of Interest (POI), constructed through three key steps: mining and aligning open-source vehicle trajectory data from GAIA with high-precision geographic POI data, rigorous manual validation of noisy spatiotemporal facts, and generating bilingual (Chinese/English) QA pairs that reflect human-understandable spatiotemporal reasoning tasks. Our dataset challenges models to parse complex spatiotemporal dependencies, and evaluations of state-of-the-art multilingual LLMs (e.g., Qwen2.5-7B, Llama3.1-8B) reveal stark limitations: even the top-performing model (Qwen2.5-7B fine-tuned with RAG+LoRA) achieves a top 10 Hit Ratio (HR@10) of only 0.41 on the easiest task, far below human performance at 0.56. This underscores persistent weaknesses in LLMs' ability to perform consistent spatiotemporal reasoning, while highlighting POI-QA as a robust benchmark to advance algorithms sensitive to spatiotemporal dynamics. The dataset is publicly available at https://www.kaggle.com/ds/7394666. |
| title | A Dataset for Spatiotemporal-Sensitive POI Question Answering |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.10928 |