For strategy 1. We first substantiate the impact of creative data diversity on model performance. We conduct experi-
ments using two subsets from the Oogiri-GO dataset: an “animal-only” subset and a “random” subset. The former consisted
of 10,000 randomly sampled data containing animal-related nouns in responses, while the latter comprised 10,000 samples
randomly drawn from the Oogiri-GO dataset. As depicted in Fig. 27 (Left), despite having an equal data size, the diverse
“random” subset exhibited significant performance advantages. Therefore, for strategy 1, collecting a wide range of potential
and diverse creative data is crucial to enhance LLM performance in generating effective new data under various weakly as-
sociated conditions. However, the inherent scarcity of high-quality creative data poses a challenge, as continuous production
of innovative data by humans is not easily sustained. Additionally, the Oogiri-GO dataset already encompasses responses
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