# Sovereign Large Language Models: Advantages, Strategy and Regulations

arXiv: https://arxiv.org/abs/2503.04745 · HTML: https://mlsanity.com/paper/2503.04745/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 170 references that are in the map.
- First posted on arXiv 5 Feb 2025.

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

- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [ALLaM: Large Language Models for Arabic and English](https://mlsanity.com/paper/2407.15390/) (2024)
- [On Large Language Models in National Security Applications](https://mlsanity.com/paper/2407.03453/) (2024)
- [KoreALBERT: Pretraining a Lite BERT Model for Korean Language Understanding](https://mlsanity.com/paper/2101.11363/) (2021)
- [OpenThaiGPT 1.5: A Thai-Centric Open Source Large Language Model](https://mlsanity.com/paper/2411.07238/) (2024)

## Built on by (most cited first, 1 of 1)

- [Jupiter-N Technical Report](https://mlsanity.com/paper/2604.17429/) (2026)

## Abstract (arXiv)

This report analyzes key trends, challenges, risks, and opportunities associated with the development of Large Language Models (LLMs) globally. It examines national experiences in developing LLMs and assesses the feasibility of investment in this sector. Additionally, the report explores strategies for implementing, regulating, and financing AI projects at the state level.