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PoG is a novel method that enhances LLM reasoning by integrating knowledge reasoning paths from KGs during the inference stage. It tackles multi-hop and multi-entity questions through dynamic multi-hop path exploration and efficient pruning techniques, allowing LLMs to access factual knowledge for more faithful and interpretable outputs without retraining. The primary goal is to improve LLM reasoning performance for knowledge-intensive tasks like KGQA.
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