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://github.com/sail-sg/CLoT.

翻译：链式思维（Chain-of-Thought, CoT）引导大语言模型逐步推理，可激发其逻辑推理能力。尽管CoT在逻辑任务中有效，却不利于需要跳出常规思维的创造性问题解决，而此类思维对创新突破至关重要。本文探索了大语言模型中的跳跃思维（Leap-of-Thought, LoT）能力——一种涉及强关联与知识跃迁的非顺序创造性范式。为此，我们以热门Oogiri游戏为研究对象，该游戏要求参与者具备出色的创造力和联想能力，对给定图像、文本或两者同时做出意外且幽默的回应，因而适合研究LoT能力。为探究大语言模型在Oogiri游戏中的LoT能力，我们首先构建了多模态多语言数据集Oogiri-GO，包含超过13万个Oogiri游戏样本，并观察到现有大语言模型在此游戏中的LoT能力不足或完全失效。据此，我们提出创意跳跃思维（Creative Leap-of-Thought, CLoT）范式以提升大语言模型的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，用于训练预训练大语言模型，使其具备特定的LoT幽默生成与判别能力。随后，CLoT设计了一种探索性自我优化机制，通过鼓励大语言模型探索看似无关概念间的平行关系生成更具创意的LoT数据，并筛选高质量数据供模型自我训练优化。CLoT不仅在Oogiri游戏的幽默生成中表现卓越，还提升了云端猜词游戏及发散性联想任务等多种任务的创造力。这些发现深化了我们的认知，并为跨领域创新应用中提升大语言模型的创造能力提供了路径。数据集、代码及模型将在线发布：https://github.com/sail-sg/CLoT。