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A Harvard research paper introduces Recoding-Decoding (RD), a novel decoding scheme that injects random priming phrases and diverting tokens to tap into an LLM's long-tail knowledge, significantly boosting output diversity without fine-tuning. The method maintains high relevance while mitigating response homogenization, with stronger models showing greater diversity gains.