## 6 Conclusions  
In this study, we explored the use of LLMs to support navigation and guidance in MASSs. Given the significant computational requirements of LLMs, we proposed a framework for LLM-assisted navigation for connected MASSs, wherein LLMs are deployed onshore or in remote clouds to facilitate navigation and provide guidance services. Additionally, certain large oceangoing vessels can deploy LLMs locally, to obtain real-time navigation recommendations. MASSs units transmit assistance requests to LLMs, which process these requests and return guidance. To assess the LLMs' knowledge and suitability for the navigation assistance system, we designed and conducted navigation theory tests comprising over 1500 multiple-choice questions, similar in format to the official exams for the OOW certificate under the STCW. Our experiments evaluated the performance of 14 LLMs, including GPT-3.5-turbo, GPT-4, GPT-4o, ERINE-4.0-8k, and Qwen-turbo, among others. The performance metrics included accuracy, cost, and processing latency. Among all the tested models, only GPT-4o achieved a passing score with an accuracy of 86%, suggesting its potential for supporting autonomous ship navigation and guidance systems. Although the experimental results indicate that most large language models (LLMs) perform relatively poorly on multiple-choice questions related to ship navigation knowledge, the success of GPT-4o highlights the promise of LLMs in ship navigation tasks. These findings underscore the necessity for further fine-tuning and optimization of model architectures to enhance the capabilities of LLMs in navigation tasks. As the maritime industry moves towards greater automation and intelligence, ensuring the safety and reliability of LLM-assisted systems is crucial. Therefore, future work should focus on advancing LLM capabilities to meet the stringent demands of safe ship navigation. At the same time, we will also address the ethical, safety, and privacy issues associated with the application of LLMs in ship navigation. We plan to tackle these issues by clarifying responsibilities, ensuring transparency in decision making, maintaining system reliability, and implementing data protection and privacy measures. Conceptualization, J.H. ; Methodology, D.P. ; Validation, D.P. ; Formal analysis, D.P. ; Investigation, D.P. ; Writing--original draft, D.P. ; Writing--review & editing, J.H., M.C. and S.Z. ; Supervision, J.H. and K.L. ; Project administration, J.H. and K.L. ; Funding acquisition, J.H. and K.L. All authors have read and agreed to the published version of the manuscript. This work was conducted by [PERSON] during his visit to the University of Essex, supported by the China Scholarship Council. Additionally, the research received funding from the Natural Science Foundation of Hubei Province, China, under Grant No. 2021 CFA001. This work was also funded by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreements No. 824019 and No. 10102280, the Horizon Europe \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**\# Ques.**} & \multirow{2}{*}{**\# Corr.**} & \multirow{2}{*}{**Acc.**} & \multirow{2}{*}{**Time (s)**} & \multicolumn{2}{c}{**\# Total Tokens**} \\ \cline{5-6}  & & & & & **\# Input Tokens** & **\# Output Tokens** \\ \hline GLM-4-Air & 814 & 531 & 65.23\% & 1250.09 & 239,865 & 2442 \\ \hline GLM-4 & 814 & 553 & 67.94\% & 1523.23 & 239,878 & 2443 \\ \hline Qianfan-Chinese-Llama-2-7B & 814 & 341 & 41.89\% & 2364.15 & 242,846 & 2709 \\ \hline Qianfan-Chinese-Llama-2-13B & 814 & 393 & 48.28\% & 4775.56 & 242,846 & 123,678 \\ \hline Qianfan-Chinese-Llama-2-70B & 814 & 486 & 59.71\% & 5511.66 & 242,846 & 93,846 \\ \hline Meta-Llama-3-8B & 814 & 407 & 50.00\% & 2475.05 & 242,846 & 814 \\ \hline Meta-Llama-3-70B & 814 & 542 & 66.58\% & 8699.69 & 242,846 & 113,623 \\ \hline Gemma-7B-it & 814 & 361 & 44.35\% & 3588.27 & 242,846 & 32,208 \\ \hline \hline \end{tabular}
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