用检索增强生成技术从新闻中自动提取地理位置坐标。
RACCOON: A Retrieval-Augmented Generation Approach for Location Coordinate Capture from News Articles
- 通过检索位置数据库获取上下文，增强大模型生成精度。
- 在三个数据集上表现优于基线方法，准确率提升显著。
- 适合需要快速定位新闻事件地点的研究与应用。
地理编码旨在自动提取新闻文章中报告事件的地理坐标，可用于疫情监测或灾害管理。本文提出RACCOON（Retrieval-Augmented Coordinate Capture Of Online News articles），一个开源的地理编码方法，可从新闻文章中提取地理坐标。RACCOON采用检索增强生成（RAG）框架：从位置数据库中检索候选地点及其相关信息作为上下文，将该上下文、地点提及和新闻原文组合成提示词输入大语言模型（LLM），生成地理坐标。我们在三个数据集上，基于两种底层LLM、三种基线方法以及多个组件消融实验对RACCOON进行了评估，结果表明其有效性。据我们所知，RACCOON是首个使用预训练LLM的RAG式地理编码方法。
原文摘要 · Abstract (English)
Geocoding involves automatic extraction of location coordinates of incidents reported in news articles, and can be used for epidemic intelligence or disaster management. This paper introduces Retrieval-Augmented Coordinate Capture Of Online News articles (RACCOON), an open-source geocoding approach that extracts geolocations from news articles. RACCOON uses a retrieval-augmented generation (RAG) approach where candidate locations and associated information are retrieved in the form of context from a location database, and a prompt containing the retrieved context, location mentions and news articles is fed to an LLM to generate the location coordinates. Our evaluation on three datasets, two underlying LLMs, three baselines and several ablation tests based on the components of RACCOON demonstrate the utility of RACCOON. To the best of our knowledge, RACCOON is the first RAG-based approach for geocoding using pre-trained LLMs.
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