# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 3 Understanding Large Language Models
### Evolution of Large Language Models  
The development of LLMs has been marked by rapid advancements in both model architecture and computational capabilities. Early language models were limited in size and scope, often constrained by hardware limitations and the availability of training data. The introduction of the transformer architecture by [PERSON] revolutionized the field by enabling more efficient training of large models through parallelization and improved handling of long-range dependencies [73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83] (Table 2) (Figure 5). \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Model** & **Reference** & **Year Released** & **Number of Parameters** & **Key Advancements** & **Relevance to Maritime Safety** \\ \hline GPT-2 & [PERSON] et al. [75] & 2019 & 1.5 billion & Improved text generation and context understanding & Basic language tasks and text summarization \\ \hline GPT-3 & [PERSON] et al. [76] & [PERSON] et al. [83] & 2020 & 175 billion & Few-shot learning, better context handling & Advanced NLP tasks, multilingual support \\ \hline GPT-3 & [PERSON] et al. [77] & 2021 & 175 billion & Enhanced conversation abilities, instruction following & Interactive dialogue, better assistance \\ \hline GPT-4 & [PERSON] et al. [83] & 2023 & Estimated over 1 trillion & Multimodal inputs, improved reasoning & Complex analysis, integration with other data types \\ \hline Specialized Maritime LLMs & [PERSON] et al. [79] & Future development & Varies & Domain-specific knowledge, terminology adaptation & Tailored applications for maritime operations \\ \hline GPT-4 Turbo & [PERSON] et al. [83] & 2024 & Over 1 trillion & Reduced operational costs, increased speed & Integration in real-time operations \\ \hline Claude 3 & [PERSON] et al. [83] & 2024 & Not disclosed & Enhanced language interpretation & Support for detailed text analysis \\ \hline LLMA 2 (Meta) & [PERSON] et al. [79] & 2024 & 70 billion & Specialization in domain-specific tasks & Analysis of technical data in maritime logs \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evolution of Large Language Models. The timeline in Figure 5 categorizes LLM development into three key phases:  
1. Early Development (2020)--The release of GPT-3 marked a major milestone in natural language processing, introducing significantly improved text generation and efficiency. This stage laid the groundwork for subsequent models by demonstrating the feasibility of large-scale language modeling for complex reasoning tasks. 2. Growth Phase (2023)--The introduction of GPT-4 Turbo, Claude 3, and LLaMA 2 signaled a period of rapid advancements. GPT-4 Turbo featured enhanced multimodal capabilities and domain-specific tuning, while Claude 3 focused on faster processing and improved summarization. LLaMA 2, developed as an open-source model, emphasized scalability for research and broader accessibility. 3. Maritime Relevance (2023 onward)--The final phase represents the adaptation of LLMs for maritime operations. Fine-tuning processes have enabled the development of customized LLMs tailored for navigation, compliance monitoring, and enhanced safety protocols. This shift signifies the growing interest in AI-driven maritime applications. While Table 2 provides a structured breakdown of LLM advancements, Figure 5 contextualizes these developments within a broader timeline. The tabular representation focuses on individual models, listing their technical parameters and maritime-specific use cases. In contrast, the visual timeline highlights the progressive nature of AI evolution, emphasizing how and when these advancements became relevant to the maritime sector. For example, Table 2 describes how GPT-4 Turbo introduced domain-specific tuning, while Figure 5 positions this within the larger framework of LLM growth leading to maritime safety innovations. OpenAI's GPT (Generative Pre-trained Transformer) [80,81] series exemplifies the evolution of LLMs.