让我们跳出框框思考：通过创意幽默生成探索大型语言模型中的跳跃思维
Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation
AI总结：
本文探索大型语言模型的跳跃思维（LoT）能力，构建多模态大喜利数据集并提出创造性跳跃思维（CLoT）范式，通过指令微调与探索性自我精炼显著提升了模型在幽默生成及多种任务中的创造力。
摘要：
思维链引导大型语言模型逐步推理，能够激发其逻辑推理能力。虽然对逻辑任务有效，但思维链不利于创造性问题的解决，而后者通常需要跳出框框的思考，且对创新进步至关重要。在本文中，我们探索了大型语言模型内的跳跃思维（LoT）能力——这是一种涉及强关联和知识跳跃的非连续创造性范式。为此，我们在流行的大喜利游戏上研究大型语言模型，该游戏要求参与者具备良好的创造力和强联想思维，以对给定的图像、文本或两者做出出人意料且幽默的反应，因此适合用于跳跃思维研究。为了调查大型语言模型在大喜利游戏中的跳跃思维能力，我们首先构建了一个多模态和多语言的大喜利-GO数据集，包含来自大喜利游戏的超过130,000个样本，并观察到大多数现有大型语言模型在大喜利游戏上跳跃思维能力不足或失败。因此，我们引入了一种创造性跳跃思维范式来提高大型语言模型的跳跃思维能力。创造性跳跃思维首先将大喜利-GO数据集转化为面向跳跃思维的指令微调数据，以训练预训练大型语言模型，使其获得一定的跳跃思维幽默生成和判别能力。然后，创造性跳跃思维设计了一种探索性自我精炼，鼓励大型语言模型通过探索看似无关概念之间的相似性来生成更具创造性的跳跃思维数据，并选择高质量数据来训练自身以进行自我精炼。创造性跳跃思维不仅在大喜利游戏的幽默生成方面表现出色，还提升了猜云游戏和发散联想任务等各种任务的创造能力。这些发现增进了我们的理解，并为提升大型语言模型的创造能力以应用于跨领域的创新提供了一条途径。数据集、代码和模型将在线发布。https://zhongshsh.github.io/CLoT/.
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
