# Leveraging Large Language Models to Geolocate Linguistic Variations in Social Media Posts

arXiv: https://arxiv.org/abs/2407.16047 · HTML: https://mlsanity.com/paper/2407.16047/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Builds on (most cited first, 5 of 5)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language](https://mlsanity.com/paper/2312.09993/) (2023)
- [Advanced Natural-based interaction for the ITAlian language: LLaMAntino-3-ANITA](https://mlsanity.com/paper/2405.07101/) (2024)
- [Camoscio: an Italian Instruction-tuned LLaMA](https://mlsanity.com/paper/2307.16456/) (2023)

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

- [Locatability-Guided Adaptive Reasoning for Image Geo-Localization with Vision-Language Models](https://mlsanity.com/paper/2603.13628/) (2026)
- [Benchmarking Large Language Models for Geolocating Colonial Virginia Land Grants](https://mlsanity.com/paper/2508.08266/) (2025)

## Abstract (arXiv)

Geolocalization of social media content is the task of determining the geographical location of a user based on textual data, that may show linguistic variations and informal language. In this project, we address the GeoLingIt challenge of geolocalizing tweets written in Italian by leveraging large language models (LLMs). GeoLingIt requires the prediction of both the region and the precise coordinates of the tweet. Our approach involves fine-tuning pre-trained LLMs to simultaneously predict these geolocalization aspects. By integrating innovative methodologies, we enhance the models' ability to understand the nuances of Italian social media text to improve the state-of-the-art in this domain. This work is conducted as part of the Large Language Models course at the Bertinoro International Spring School 2024. We make our code publicly available on GitHub https://github.com/dawoz/geolingit-biss2024.