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The article presents R2VC, a modular fact-checking system that combines retrieval, verification, and confidence calibration to improve accuracy and reliability in automated fact checking, achieving a 13.74% accuracy increase on the FEVER benchmark.
This paper presents a modular pipeline for educational analogy generation, decomposing the task into four stages and evaluating 12 LLMs and 7 embedding models. Results show that sub-concept grounding improves explanation quality and retrieval precision, with a novel LLM-as-a-judge evaluation validated against human annotations.