4. Creative Leap-of-Thought (CLoT)

To augment the Leap-of-Thought (LoT) ability in (multi-
modal) Large Language Models (LLMs) for creative gener-
ation, we propose a novel Creative LoT framework (CLoT).
As shown in Fig. 4, CLoT relies on two LoT-boosting
stages. The first one is associable instruction tuning that for-
mulates the Oogiri-GO dataset into instruction tuning data
for training an LLM to improve its LoT ability (Sec. 4.1).
The second one is explorative self-refinement
that en-
courages the LLM to generate more creative LoT data
via exploring parallels between seemingly unrelated con-
cepts, and selects high-quality data to train itself for self-
refinement (Sec. 4.2). Finally, we present the CLoT infer-
ence to induce the LoT ability of the trained LLM (Sec. 4.3).

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Let’sThinkOutsidetheBox