KRAIL combines LLM agents, few-shot prompting, and a Neo4j knowledge graph built from IDHEAS-DATA to predict the attributes needed to look up base human error probabilities, cutting analysis time to under 150 seconds.
US Nuclear Regulatory Commission, Washington, DC
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KRAIL: A Knowledge-Driven Framework for Base Human Reliability Analysis Integrating IDHEAS and Large Language Models
KRAIL combines LLM agents, few-shot prompting, and a Neo4j knowledge graph built from IDHEAS-DATA to predict the attributes needed to look up base human error probabilities, cutting analysis time to under 150 seconds.