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The article discusses the challenges and solutions in injecting domain-specific knowledge into large language models (LLMs) to enhance their performance on specialized benchmarks. It highlights the critical balance needed in knowledge infusion to avoid 'memory collapse' due to over-infusion. The authors present a knowledge infusion scaling law that predicts the optimal amount of domain knowledge to be integrated during pre-training, supported by experiments showing consistent results across different model sizes.