Chain-of-Thought (CoT) 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 non-sequential, 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 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/.

翻译：链式思维（Chain-of-Thought, CoT）引导大型语言模型（LLMs）逐步推理，并能激发其逻辑推理能力。尽管CoT在逻辑任务中效果显著，但不利于需要突破性思维的创造性问题解决，而后者对创新进步至关重要。本文探索了LLMs中的“思维飞跃”（Leap-of-Thought, LoT）能力——一种涉及强关联与知识跳跃的非线性创造性范式。为此，我们以流行的Oogiri游戏为研究对象，该游戏要求参与者具备良好的创造力和强联想能力，能够对给定图像、文本或两者做出出人意料且幽默的回应，因此适合LoT研究。为探究LLMs在Oogiri游戏中的LoT能力，我们首先构建了多模态、多语言的Oogiri-GO数据集，包含超过13万个来自Oogiri游戏的样本，并观察到现有大部分LLMs在Oogiri游戏中LoT能力不足或失败。据此，我们提出了一种创意思维飞跃（Creative Leap-of-Thought, CLoT）范式以提升LLM的LoT能力。CLoT首先将Oogiri-GO数据集转化为面向LoT的指令微调数据，训练预训练LLM获得一定的LoT幽默生成与判别能力；随后设计探索性自我优化策略，通过鼓励LLM探索看似无关概念间的平行关系生成更具创意的LoT数据，并筛选高质量数据用于自我训练以实现自我提升。CLoT不仅在Oogiri游戏的幽默生成中表现优异，还在云猜谜游戏和发散联想任务等多种任务中提升了创造性能力。这些发现深化了我们的理解，并为跨领域创新应用中提升LLM的创造能力提供了可行路径。数据集、代码与模型将在线发布。https://zhongshsh.github.io/CLoT/。