knowledge outside of traditional cognitive limitations. The
exploration ability distinguishes our CLoT from CoT which
primarily guides the LLM to exploit its inherent reasoning
ability without emphasizing knowledge exploration.
(2) Self-refinement. Here we combine the above gener-
ated instructions with vanilla instruction tuning samples in
Sec. 4.1 to form a dataset with more than 550,000 samples
to train our LLM again. Since the above generated data is of
high diversity because of its exploration strategy, they pre-
vent performance collapse [75, 76] during self-refinement
phase, and can improve the LoT performance across several
creative tasks as shown in Sec. 5. See the ablation study and
more discussions in Sec. 5.5.
4.3. CLoT Inference
After the two LoT-boosting phases in Sec. 4.1 and 4.2, the
LLM acquires sufficient LoT ability. Now we introduce the
inference steps of LLM to release its LoT ability. Formally,
given an Oogiri user-input I of the formation in Fig. 5,
Let’sThinkOutsidetheBox