# Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages

arXiv: https://arxiv.org/abs/2310.09340 · HTML: https://mlsanity.com/paper/2310.09340/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2023.

## Built on by (most cited first, 30 of 31)

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- [Evaluating Large Language Models on Spatial Tasks: A Multi-Task Benchmarking Study](https://mlsanity.com/paper/2408.14438/) (2024)
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- [A systematic review of geospatial location embedding approaches in large language models: A path to spatial AI systems](https://mlsanity.com/paper/2401.10279/) (2024)
- [Evaluation of Geographical Distortions in Language Models](https://mlsanity.com/paper/2404.17401/) (2024)
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- [Foundation Models for Environmental Science: A Survey of Emerging Frontiers](https://mlsanity.com/paper/2504.04280/) (2025)
- [Where Do We Go from Here? Multi-scale Allocentric Relational Inference from Natural Spatial Descriptions](https://mlsanity.com/paper/2402.16364/) (2024)
- [Monitoring Critical Infrastructure Facilities During Disasters Using Large Language Models](https://mlsanity.com/paper/2404.14432/) (2024)
- [Foundation Models for Autonomous Robots in Unstructured Environments](https://mlsanity.com/paper/2407.14296/) (2024)
- [Beyond Words: Evaluating Large Language Models in Transportation Planning](https://mlsanity.com/paper/2409.14516/) (2024)
- [DisasterTD: Disaster Toponym Disambiguation Using Multimodal LLMs and Cross-View Geolocalization](https://mlsanity.com/paper/2607.24856/) (2026)
- [Exploring Spatial Representations in the Historical Lake District Texts with LLM-based Relation Extraction](https://mlsanity.com/paper/2406.14336/) (2024)
- [Geospatial Mechanistic Interpretability of Large Language Models](https://mlsanity.com/paper/2505.03368/) (2025)
- [Into the Unknown: Generating Geospatial Descriptions for New Environments](https://mlsanity.com/paper/2406.19967/) (2024)
- [Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Events](https://mlsanity.com/paper/2411.12880/) (2024)
- [Toward building next-generation Geocoding systems: a systematic review](https://mlsanity.com/paper/2503.18888/) (2025)
- [Georeferencing complex relative locality descriptions with large language models](https://mlsanity.com/paper/2512.14228/) (2025)
- [Thinking on Maps: How Foundation Model Agents Explore, Remember, and Reason Map Environments](https://mlsanity.com/paper/2512.24504/) (2025)
- [Large Language Models for Geolocation Extraction in Humanitarian Crisis Response](https://mlsanity.com/paper/2602.08872/) (2026)
- [Artificial Intelligence and Human Geography](https://mlsanity.com/paper/2312.08827/) (2023)
- [Coordinates from Context: Using LLMs to Ground Complex Location References](https://mlsanity.com/paper/2510.08741/) (2025)
- [ALIGN: A Vision-Language Framework for High-Accuracy Accident Location Inference through Geo-Spatial Neural Reasoning](https://mlsanity.com/paper/2511.06316/) (2025)
- [Extracting Disaster Impacts and Impact Related Locations in Social Media Posts Using Large Language Models](https://mlsanity.com/paper/2511.21753/) (2025)

## Abstract (arXiv)

Social media messages posted by people during natural disasters often contain important location descriptions, such as the locations of victims. Recent research has shown that many of these location descriptions go beyond simple place names, such as city names and street names, and are difficult to extract using typical named entity recognition (NER) tools. While advanced machine learning models could be trained, they require large labeled training datasets that can be time-consuming and labor-intensive to create. In this work, we propose a method that fuses geo-knowledge of location descriptions and a Generative Pre-trained Transformer (GPT) model, such as ChatGPT and GPT-4. The result is a geo-knowledge-guided GPT model that can accurately extract location descriptions from disaster-related social media messages. Also, only 22 training examples encoding geo-knowledge are used in our method. We conduct experiments to compare this method with nine alternative approaches on a dataset of tweets from Hurricane Harvey. Our method demonstrates an over 40% improvement over typically used NER approaches. The experiment results also show that geo-knowledge is indispensable for guiding the behavior of GPT models. The extracted location descriptions can help disaster responders reach victims more quickly and may even save lives.