# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 7 Future Directions and Recommendations
### Critique of LLM Limitations  
While Large Language Models (LLMs) offer transformative capabilities, their deployment in safety-critical environments such as maritime operations necessitates a critical evaluation of their limitations. These challenges, if left unaddressed, could undermine their reliability and effectiveness, leading to potentially serious operational risks. In this section, we analyze three key issues: hallucinations, model bias, and over-reliance on automation, each of which poses significant concerns in the maritime context. 7.5.1. Hallucinations and the Risk of Incorrect Outputs  
The tendency of Large Language Models (LLMs) to hallucinate, generating outputs that are plausible but factually incorrect, is a well-documented limitation. This issue is particularly concerning in sensitive applications, such as maritime operations, where accurate and timely information is crucial. Hallucinations in LLMs can lead to flawed recommendations, potentially resulting in severe consequences [122; 225]. For instance, an LLM analyzing weather data might misinterpret patterns or generate erroneous forecasts, leading to unsafe navigational decisions [215]. Additionally, incorrect interpretations of regulatory documents could result in non-compliance with international maritime laws, exposing operators to legal liabilities and operational risks [218]. The underlying cause of hallucinations stems from the probabilistic nature of LLMs, which predict the most likely sequence of words based on their training data without inherent understanding [1]. To mitigate this risk, rigorous validation mechanisms, such as cross verification with trusted sources and integrating Explainable AI (XAI) techniques, are recommended [129; 214]. 7.5.2. Model Bias and Its Implications in a Multicultural Industry  
Model bias is another critical limitation of LLMs, particularly in industries like maritime operations that involve a diverse, multicultural workforce. Biases in LLMs arise from imbalanced or non-representative training datasets, which can encode societal, linguistic, or cultural prejudices. For example, an LLM trained predominantly on English-language data may exhibit reduced performance or inaccuracies when processing input in less common languages or dialects. This can exacerbate communication barriers among multi-lingual crews and hinder the effectiveness of AI-assisted decision-making [200; 201]. Bias in regulatory interpretation is another concern. Regulatory documents often reflect jurisdictional nuances, and an LLM may prioritize certain interpretations over others, potentially disadvantaging specific operators or vessel types [202]. To address these issues, training datasets must be curated to reflect the linguistic, cultural, and regulatory diversity of the maritime industry. This includes incorporating multilingual datasets, diverse regulatory frameworks, and region-specific operational records. Regular bias audits are essential to identify and mitigate any unintended disparities in the model's performance [203; 214]. Furthermore, feedback loops involving diverse end users, such as vessel crews and port authorities, can help refine the model and ensure equitable outcomes [205]. 7.5.3. Over-Reliance on Automation and the Erosion of Human Expertise  
The increasing capabilities of LLMs raise concerns about over-reliance on automation, which could erode human expertise and critical thinking skills over time. In maritime operations, where decision-making often occurs under high-pressure conditions, the tendency to defer to AI recommendations without adequate scrutiny poses significant risks. For example, an over-reliant operator might accept an AI-generated risk assessment without questioning its validity, even if the underlying data or assumptions are flawed [216]. While LLMs can automate routine tasks, such as generating reports or analyzing data, their outputs must be viewed as tools to augment human decision-making, not replace it.