用大模型解析复杂地点描述，精准定位真实地理坐标
Coordinates from Context: Using LLMs to Ground Complex Location References
- 基于大模型上下文理解能力，实现复合地点描述的地理定位
- 微调小模型即可达到大型现成模型的精度水平
- 适合需要高精度地点解析的自然语言处理与地理信息分析场景
地理编码是将地点描述映射到实际地理坐标的任务，对非结构化文本的下游分析至关重要。本文研究复合地点描述的地理编码难题。基于近期研究表明大模型具备空间推理能力，我们评估了大模型在地理知识与空间推理方面的能力。在此基础上，提出一种基于大模型的复合地点描述地理编码策略。实验表明，该方法显著提升任务性能，且经过微调的小型模型可达到远大于其规模的现成大模型的性能水平。
原文摘要 · Abstract (English)
Geocoding is the task of linking a location reference to an actual geographic location and is essential for many downstream analyses of unstructured text. In this paper, we explore the challenging setting of geocoding compositional location references. Building on recent work demonstrating LLMs' abilities to reason over geospatial data, we evaluate LLMs' geospatial knowledge versus reasoning skills relevant to our task. Based on these insights, we propose an LLM-based strategy for geocoding compositional location references. We show that our approach improves performance for the task and that a relatively small fine-tuned LLM can achieve comparable performance with much larger off-the-shelf models.
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