Rule2Text generates and evaluates natural language explanations of knowledge graph rules, finding that chain-of-thought prompting with entity types works best and that fine-tuning Zephyr on LLM-built ground truth sharply raises automatic metrics.
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Rule2Text: A Framework for Generating and Evaluating Natural Language Explanations of Knowledge Graph Rules
Rule2Text generates and evaluates natural language explanations of knowledge graph rules, finding that chain-of-thought prompting with entity types works best and that fine-tuning Zephyr on LLM-built ground truth sharply raises automatic metrics.