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arXiv AI By Alexander Thomas, Hubert P. H. Shum, Darren Nellis, Manli Zhu, Phatpicha Yochum, William Bartle, Daniel Wrightson

Evaluating Large Language Model Performance on International Maritime Dangerous Goods Code Compliance

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The paper introduces DGEval, a benchmark of 1,678 questions designed to assess large language models (LLMs) on the International Maritime Dangerous Goods (IMDG) Code Amendment 42‑24. It evaluates 13 models from six providers, finding that while the best model surpasses human practitioners on multiple‑choice tasks, all models perform poorly on safety‑critical areas such as stowage, segregation, and regulatory recall. The study concludes that LLMs can aid compliance tasks—especially structured Dangerous Goods List lookups with web search—but human oversight and authoritative source verification remain essential for safety‑critical deployment.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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