| Challenge: | Existing methods rely on text retrieval and geographic knowledge bases to generate coordinates, and they are prone to error propagation and dependency on structured knowledge bases. |
| Approach: | They propose to use large language models to convert geographic coordinates into geohash sequences and introduce a Chain-of-Thought mechanism to enhance the model’s reasoning over spatial relationships. |
| Outcome: | The proposed framework can handle explicit address queries in single-point predictions and effectively resolve vague relative location queries. |
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| Challenge: | Existing approaches to address address standardization are lacking in the current field. |
| Approach: | They propose a framework that incorporates spatial knowledge into address texts and achieves efficient address standardization. |
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| Challenge: | Existing geocoding tools can only link locations already in a geographic database, which often do not include compositional locations. |
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| Challenge: | Existing approaches to geocoding only encode location mentions and their context . |
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AgentMove: A Large Language Model based Agentic Framework for Zero-shot Next Location Prediction (2025.naacl-long)
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