# Comparative Performance of Advanced NLP Models and LLMs in Multilingual Geo-Entity Detection

arXiv: https://arxiv.org/abs/2412.20414 · HTML: https://mlsanity.com/paper/2412.20414/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 29 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding](https://mlsanity.com/paper/2310.14478/) (2023)
- [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://mlsanity.com/paper/2110.08151/) (2021)

## Built on by (most cited first, 2 of 2)

- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (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)

The integration of advanced Natural Language Processing (NLP) methodologies and Large Language Models (LLMs) has significantly enhanced the extraction and analysis of geospatial data from multilingual texts, impacting sectors such as national and international security. This paper presents a comprehensive evaluation of leading NLP models -- SpaCy, XLM-RoBERTa, mLUKE, GeoLM -- and LLMs, specifically OpenAI's GPT 3.5 and GPT 4, within the context of multilingual geo-entity detection. Utilizing datasets from Telegram channels in English, Russian, and Arabic, we examine the performance of these models through metrics such as accuracy, precision, recall, and F1 scores, to assess their effectiveness in accurately identifying geospatial references. The analysis exposes each model's distinct advantages and challenges, underscoring the complexities involved in achieving precise geo-entity identification across varied linguistic landscapes. The conclusions drawn from this experiment aim to direct the enhancement and creation of more advanced and inclusive NLP tools, thus advancing the field of geospatial analysis and its application to global security.