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This method is a variant of the KG-LLM Framework, specifically designed with a simplified "ablation knowledge prompt." Unlike the full KG-LLM prompt, it removes explicit instructions, textualized IDs, and CoT reasoning from the expected response. It is introduced and evaluated by the authors as a baseline within their proposed approach to demonstrate the effectiveness of the advanced prompting strategies in the full KG-LLM Framework for multi-hop link prediction.
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The KG-LLM Framework is a novel method that converts structured KG data (multi-hop paths) into natural language Chain-of-Thought (CoT) prompts. These prompts are then used to instruction fine-tune LLMs to enhance multi-hop link prediction and relation prediction performance in KGs. The method leverages LLMs' generative and reasoning capabilities to solve KG completion tasks.
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