Abstract

Chain-of-Thought (CoT) [2, 3] guides large language models (LLMs) to reason step-by-step, and can motivate their logical reasoning ability. While effective for logical tasks, CoT is not conducive to creative problem-solving which often requires out-of-box thoughts and is crucial for innovation advancements. In this paper, we explore the Leap-of-Thought (LoT) abilities within LLMs — a nonsequential, creative paradigm involving strong associations and knowledge leaps. To this end, we study LLMs on the popular Oogiri game which needs participants to have good creativity and strong associative thinking for responding unexpectedly and humorously to the given image, text, or both, and thus is suitable for LoT study. Then to investigate LLMs’ LoT ability in the Oogiri game, we first build a multimodal and multilingual Oogiri-GO dataset which contains over 130,000 samples from the Oogiri game, and observe the insufficient LoT ability or failures of most existing LLMs on the Oogiri game. Accordingly, we introduce a creative Leap-of-Thought (CLoT) paradigm to improve LLM’s LoT ability. CLoT first formulates the Oogiri-GO dataset into LoT-oriented instruction tuning data to train pretrained LLM for achieving certain LoT humor generation and discrimination abilities. Then CLoT designs an explorative self-refinement that encourages the LLM to generate more creative LoT data via exploring parallels between seemingly unrelated concepts and selects high-quality data to train itself for self-refinement. CLoT not only excels in humor generation in the Oogiri game as shown in Fig. 1 but also boosts creative abilities in various tasks like “cloud guessing game” and “divergent association task”. These findings advance our understanding and offer a pathway to improve LLMs’ creative capacities for innovative applications across domains. The dataset, code, and models will be released online. https://zhongshsh.github.io/ CLoT/.

Framework

Experiment

Conclusion

In this paper, we propose a Creative Leap-of-Thought (CLoT) paradigm to improve LLM’s leap-of-thought (LoT) ability. CLoT first collects a multimodal Oogiri-GO dataset, and formulates it into instruction tuning data to train LLM to improve its LoT ability. Then CLoT designs an explorative self-refinement that lets LLM generate more creative LoT data via exploring parallels among different concepts and selects high-quality data to train itself for self-refinement. Experimental results show the effectiveness and generalization ability of CLoT across several creative tasks.