HUMORCHAIN: 基于理论的多阶段推理用于可解释的多模态幽默生成
HUMORCHAIN: Theory-Guided Multi-Stage Reasoning for Interpretable Multimodal Humor Generation
AI总结：
本文提出HUMORCHAIN框架，通过整合视觉语义解析、基于幽默和心理学的推理及细调判别器，实现可解释的多模态幽默生成，实验表明其在人类幽默偏好、Elo/BT评分和语义多样性上优于现有方法。
摘要：
幽默作为人类创造性的活动和社会凝聚力机制，长期以来一直是AI生成的主要挑战。尽管生成幽默需要复杂的认知推理和社会理解，但幽默理论表明它遵循可学习的模式和结构，使生成模型能够隐式地获取它们。近年来，多模态幽默已成为在线通信的常见形式，尤其是在Z世代中，凸显了需要能够整合视觉理解和幽默语言生成的AI系统的需求。然而，现有数据驱动的方法缺乏对幽默的显式建模或理论基础，通常生成直白的描述，无法捕捉其背后的认知机制，导致生成的图像描述流畅但缺乏真正的幽默或认知深度。为了解决这一限制，我们提出了HUMORCHAIN（HUmor-guided Multi-step Orchestrated Reasoning Chain for Image Captioning），一种基于理论的多阶段推理框架。它整合了视觉语义解析、基于幽默和心理学的推理以及细调的幽默判别器，形成一个可解释和可控的认知推理链。据我们所知，这是首次将幽默理论中的认知结构显式嵌入多模态幽默生成的工作，使从视觉理解到幽默创造的过程结构化。在Meme-Image-No-Text、Oogiri-GO和OxfordTVG-HIC数据集上的实验表明，HUMORCHAIN在人类幽默偏好、Elo/BT评分和语义多样性上优于现有方法，证明了基于理论的结构化推理使大语言模型能够生成符合人类感知的幽默。
英文摘要：
Humor, as both a creative human activity and a social binding mechanism, has long posed a major challenge for AI generation. Although producing humor requires complex cognitive reasoning and social understanding, theories of humor suggest that it follows learnable patterns and structures, making it theoretically possible for generative models to acquire them implicitly. In recent years, multimodal humor has become a prevalent form of online communication, especially among Gen Z, highlighting the need for AI systems capable of integrating visual understanding with humorous language generation. However, existing data-driven approaches lack explicit modeling or theoretical grounding of humor, often producing literal descriptions that fail to capture its underlying cognitive mechanisms, resulting in the generated image descriptions that are fluent but lack genuine humor or cognitive depth. To address this limitation, we propose HUMORCHAIN (HUmor-guided Multi-step Orchestrated Reasoning Chain for Image Captioning), a theory-guided multi-stage reasoning framework. It integrates visual semantic parsing, humor- and psychology-based reasoning, and a fine-tuned discriminator for humor evaluation, forming an interpretable and controllable cognitive reasoning chain. To the best of our knowledge, this is the first work to explicitly embed cognitive structures from humor theories into multimodal humor generation, enabling a structured reasoning process from visual understanding to humor creation. Experiments on Meme-Image-No-Text, Oogiri-GO, and OxfordTVG-HIC datasets show that HUMORCHAIN outperforms state-of-the-art baselines in human humor preference, Elo/BT scores, and semantic diversity, demonstrating that theory-driven structured reasoning enables large language models to generate humor aligned with human perception.
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。
