Generating Realistic, Protocol-Compliant Maritime Radio Dialogues using Self-Instruct and Low-Rank Adaptation
#maritime radio #dialogue generation #Self-Instruct #Low-Rank Adaptation #protocol compliance #AI training #specialized domains
📌 Key Takeaways
- Researchers developed a method to generate realistic maritime radio dialogues using Self-Instruct and Low-Rank Adaptation (LoRA).
- The approach ensures dialogues comply with maritime communication protocols and conventions.
- It addresses the challenge of limited training data for specialized domains like maritime radio.
- The generated dialogues can be used for training AI systems and simulating maritime scenarios.
📖 Full Retelling
arXiv:2603.04423v1 Announce Type: cross
Abstract: VHF radio miscommunication remains a major safety risk in maritime operations, with human factors accounting for over 58% of recorded incidents in Europe between 2014 and 2023. Despite decades of operational use, VHF radio communications are still prone to noise, interference, linguistic variability, and the absence of real-time transcription, making procedural errors both frequent and difficult to correct. Developing AI-assisted systems to supp
🏷️ Themes
AI Generation, Maritime Communication
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Original Source
--> Computer Science > Computation and Language arXiv:2603.04423 [Submitted on 16 Feb 2026] Title: Generating Realistic, Protocol-Compliant Maritime Radio Dialogues using Self-Instruct and Low-Rank Adaptation Authors: Gürsel Akdeniz , Emin Cagatay Nakilcioglu View a PDF of the paper titled Generating Realistic, Protocol-Compliant Maritime Radio Dialogues using Self-Instruct and Low-Rank Adaptation, by G\"ursel Akdeniz and 1 other authors View PDF HTML Abstract: VHF radio miscommunication remains a major safety risk in maritime operations, with human factors accounting for over 58% of recorded incidents in Europe between 2014 and 2023. Despite decades of operational use, VHF radio communications are still prone to noise, interference, linguistic variability, and the absence of real-time transcription, making procedural errors both frequent and difficult to correct. Developing AI-assisted systems to support real-time communication and decision-making requires a considerable amount of high-quality maritime data, yet operational, regulatory, and privacy constraints render such datasets scarce. This study introduces a compliance aware Self-Instruct methodology for generating realistic maritime radio dialogues that conform to the IMO's SMCP. Our approach integrates a 26-filter verification pipeline directly into the iterative generation loop to enforce entity information accuracy, hallucination detection, SMCP-compliance, logical consistency, and linguistic diversity. We employ LORA for parameter-efficient fine-tuning, reducing computational overhead during training and enabling efficient deployment of the resulting models on resource-constrained maritime systems. To assess dataset quality, we introduce a novel evaluation framework combining automated and expert assessments: Format Accuracy, Information Accuracy, Uniqueness, and Logical Coherence. Experiments using publicly available vessel, coastal and AIS datasets demonstrate that the approach produces synthetically div...
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