given an Oogiri user-input I of the formation in Fig. 5,
LLM first uses explorative remote association in Sec. 4.2
to construct n weakly-associated conditions, and then fol-
lows Sec. 4.2 to generate n responses {Ri}n
i=1. Next, LLM
ranks these responses by using its learned ranking skill and
finally selects the top-1 one from the ranked top-2 response
via its selection skill. The reason to first use ranking be-
fore selection is that as shown by experimental results in
Sec. 5, directly choosing the best one from a large number
of options has poor accuracy, and ranking can filter out low-
quality candidates to improve the selection accuracy. See
Algorithm 1 for an overview of CLoT inference steps.
Let’sThinkOutsidetheBox