# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 7 Future Directions and Recommendations  
### Technological Advancements  
The rapid evolution of Large Language Models (LLMs) presents significant opportunities for enhancing maritime safety in the future. As LLMs continue to advance, several key developments are anticipated that will further augment their capabilities and integration within maritime operations. One potential development is the improvement of LLMs' understanding and generation of domain-specific language. Future models are expected to possess greater proficiency in maritime terminology, protocols, and regulatory language due to more specialized training datasets. This specialization will enhance their effectiveness in tasks such as interpreting complex regulations, providing precise navigational assistance, and facilitating technical communications among crew members. Another significant development is the increased computational efficiency of LLMs. Ongoing research aims to reduce the resource requirements of these models through techniques such as model distillation, quantization, and sparsity. These improvements will make it more feasible to deploy LLMs on edge devices with limited processing power, such as those found on ships, thereby enhancing real-time applications without relying heavily on cloud infrastructure. The integration of LLMs with other emerging technologies is poised to create synergistic effects that can revolutionize maritime operations. For instance, combining LLMs with the Internet of Things (IoT) can facilitate more comprehensive data collection and analysis [182]. IoT devices on vessels can generate real-time data on equipment status, environmental conditions, and operational parameters. LLMs can process these data to provide predictive insights, anomaly detection, and decision support, enhancing situational awareness and proactive risk management [183]. The integration of LLMs with maritime IoT systems represents a significant opportunity for enhancing operational safety and efficiency. By analyzing real-time sensor data alongside textual information, such systems could provide actionable insights, such as early warning of equipment failures or optimal navigation routes based on weather and traffic conditions. Blockchain technology offers another avenue for integration. The immutable and decentralized nature of blockchain can enhance data security and transparency in maritime operations. Integrating LLMs with blockchain can enable secure and verifiable communication, documentation, and transaction processes. For example, smart contracts powered by blockchain can automate compliance verification, while LLMs can interpret and execute these contracts in accordance with regulatory requirements [184]. Artificial Intelligence of Things (AIoT) [185], which combines AI with IoT infrastructure, represents a convergence of technologies that can significantly impact maritime safety. LLMs within AIoT frameworks can process vast amounts of sensor data to optimize operations, predict maintenance needs, and enhance environmental monitoring. This holistic approach can lead to smarter ships that are more efficient, safer, and environmentally friendly. Moreover, advancements in Explainable AI (XAI) are expected to address current limitations regarding the transparency of LLMs. Developing models that can provide interpretable explanations for their outputs will increase trust and acceptance among users, particularly in safety-critical maritime applications where understanding the rationale behind AI decisions is essential [103,186] (Figure 7). Developing lightweight LLMs optimized for onboard use is essential to enable real-time decision-making in resource-constrained environments. These models should leverage techniques such as quantization and sparse attention mechanisms to ensure efficiency without compromising performance. Future research should also explore hybrid models that integrate onboard and cloud-based processing to overcome connectivity challenges.