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AI Research
arXiv CS.AI·Jun 12·5 min read
Multi-Field Hybrid Retrieval-Augmented Generation for Maritime Accident Root Cause Analysis
TL;DR
Maritime accident reports are difficult to analyze due to the vast amount of historical data. A multi-field hybrid retrieval-augmented generation (RAG) framework was developed to automate root cause analysis (RCA) using a dataset of 13,329 Korea Maritime Safety Tribunal reports.
✦ Why It Matters
Engineers can leverage this RAG framework to enhance the efficiency and accuracy of maritime accident investigations.
Key Takeaways
- Utilize structured incident cards for better data organization.
- Apply Reciprocal Rank Fusion for improved retrieval accuracy.
- Leverage historical reports to enhance RCA quality.
- Adopt automated systems to reduce labor in maritime investigations.
How It Works
The framework organizes adjudication reports into structured incident cards, categorizing them into Summary, Causes, and Disposition. It employs a hybrid retrieval strategy that combines both sparse and dense ranking methods, optimizing the search for relevant precedents.
By grounding the report generation process on these retrieved precedents, the framework ensures that the generated analyses are evidence-based and consistent.