# Llamarine: Open-source Maritime Industry-specific Large Language Model

arXiv: https://arxiv.org/abs/2503.00203 · HTML: https://mlsanity.com/paper/2503.00203/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 43 references that are in the map.
- First posted on arXiv 28 Feb 2025.

## Builds on (most cited first, 17 of 17)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2306.13394/) (2023)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022)
- [Towards Generalist Biomedical AI](https://mlsanity.com/paper/2307.14334/) (2023)
- [FinGPT: Open-Source Financial Large Language Models](https://mlsanity.com/paper/2306.06031/) (2023)
- [MarineGPT: Unlocking Secrets of Ocean to the Public](https://mlsanity.com/paper/2310.13596/) (2023)
- [OSCaR: Object State Captioning and State Change Representation](https://mlsanity.com/paper/2402.17128/) (2024)
- [EAGLE: Egocentric AGgregated Language-video Engine](https://mlsanity.com/paper/2409.17523/) (2024)
- [CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models](https://mlsanity.com/paper/2304.10946/) (2023)
- [MISAR: A Multimodal Instructional System with Augmented Reality](https://mlsanity.com/paper/2310.11699/) (2023)
- [Exploring Boundary of GPT-4V on Marine Analysis: A Preliminary Case Study](https://mlsanity.com/paper/2401.02147/) (2024)
- [KUNPENG: An Embodied Large Model for Intelligent Maritime](https://mlsanity.com/paper/2407.09048/) (2024)
- [SemiKong: Curating, Training, and Evaluating A Semiconductor Industry-Specific Large Language Model](https://mlsanity.com/paper/2411.13802/) (2024)
- [Enhancing Maritime Trajectory Forecasting via H3 Index and Causal Language Modelling (CLM)](https://mlsanity.com/paper/2405.09596/) (2024)
- [Digital Twin for Autonomous Surface Vessels: Enabler for Safe Maritime Navigation](https://mlsanity.com/paper/2411.03465/) (2024)

## Built on by (most cited first, 4 of 4)

- [DREAM: Domain-aware Reasoning for Efficient Autonomous Underwater Monitoring](https://mlsanity.com/paper/2509.13666/) (2025)
- [Multi-Model Synthetic Training for Mission-Critical Small Language Models](https://mlsanity.com/paper/2509.13047/) (2025)
- [Generating Realistic, Protocol-Compliant Maritime Radio Dialogues using Self-Instruct and Low-Rank Adaptation](https://mlsanity.com/paper/2603.04423/) (2026)
- [Earth Science Foundation Models: From Perception to Reasoning and Discovery](https://mlsanity.com/paper/2605.12542/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated substantial potential in addressing complex reasoning tasks, yet their general-purpose nature often limits their effectiveness in specialized domains such as maritime navigation. To bridge this gap, we introduce Llamarine, the first open-source LLM designed specifically for maritime navigation. Llamarine 1.0 is developed through continued pretraining and fine-tuning on a high-quality corpus comprising maritime textbooks, research publications, and web text from Wikipedia. This domain-specific training enables the model to acquire expert-level knowledge in navigational principles, collision avoidance, route optimization, and regulatory compliance. Our key contributions include (a) the curation of a comprehensive maritime dataset from authoritative sources, ensuring depth and reliability in the model's knowledge base; (b) the development of a foundational model capable of reasoning about complex navigational challenges with greater accuracy than general-purpose LLMs; and (c) the establishment of a benchmark to evaluate performance in maritime-specific decision-making tasks. Experimental results demonstrate that Llamarine outperforms both general-purpose and commercial LLMs in critical navigation-related tasks, such as trajectory planning, risk assessment, and compliance with maritime regulations. By providing an open-source foundation model trained exclusively on high-quality maritime literature, Llamarine paves the way for AI-driven advancements in maritime safety, efficiency, and operational decision-making.