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MARS adapts retrieval depth dynamically, achieving state-of-the-art KGQA performance without the overhead of model fine-tuning.
LLMs can now reason over complex knowledge graphs with unseen entities and relations, outperforming traditional embedding methods, by learning to "think" through chain-of-thought prompts.