利用自指导和低秩适应生成符合协议的海上无线电对话
Generating Realistic, Protocol-Compliant Maritime Radio Dialogues using Self-Instruct and Low-Rank Adaptation
AI总结：
本研究提出一种自指导和低秩适应方法，生成符合IMO SMCP协议的海上无线电对话，通过整合验证流程和高效微调技术，提升对话生成的准确性和合规性。
摘要：
VHF无线电沟通在海上运营中仍然是重大的安全风险，人类因素在欧洲2014至2023年间记录的事故中占超过58%。尽管已经使用了数十年，VHF无线电通讯仍然容易受到噪声、干扰、语言变异和缺乏实时转录的影响，使程序错误频繁且难以纠正。开发辅助实时通信和决策的AI系统需要大量高质量的航海数据，但运营、监管和隐私限制使得此类数据集稀缺。本研究介绍了一种符合IMO SMCP的自指导方法，用于生成符合协议的海上无线电对话。我们的方法将一个26过滤验证流程直接整合到迭代生成循环中，以确保实体信息准确、幻觉检测、SMCP合规性、逻辑一致性和语言多样性。我们采用LORA进行参数高效微调，减少训练过程中的计算开销，并使生成的模型能够高效部署在资源受限的航海系统上。为了评估数据集质量，我们引入了一种结合自动化和专家评估的新评估框架：格式准确度、信息准确度、唯一性和逻辑一致性。使用公开可用的船舶、海岸和AIS数据集进行实验表明，该方法能够生成合成多样化、程序合规且操作真实的对话。尽管下游应用如自动语音识别和自然语言处理留待未来工作，但发布的代码、数据集和验证工具为人工智能辅助的航海安全和其他安全关键领域提供了可重复的基础。
英文摘要：
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 diverse, procedurally compliant, and operationally realistic dialogues. Although downstream applications such as automatic speech recognition and natural language processing are reserved for future work, the released code, datasets, and verification tools provide a reproducible foundation for artificial intelligence-assisted maritime safety and other safety-critical domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
