破解 juxtaposition 的密码：AI 模型能否理解幽默的矛盾
Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions
AI总结：
本文探讨了AI在理解基于矛盾叙事的幽默中的挑战，通过引入YesBut基准测试，评估大型语言模型在识别和解释漫画中的表现，发现即使是最先进的模型仍无法匹敌人类水平。
摘要：
近年来，大型多模态语言模型在广泛任务中表现出色。然而，这些模型在理解通过 juxtaposition 产生的幽默方面仍存在困难，尤其是涉及非线性叙事的笑话和幽默提示。本文通过聚焦包含矛盾叙事的漫画，其中每幅漫画由两幅面板构成，产生幽默矛盾，引入了YesBut基准测试，旨在评估AI在识别和解释这些漫画方面的能力，从字面内容理解到深层叙事推理。通过广泛实验和分析近期商业或开源的大型（视觉）语言模型，我们评估了它们理解这些漫画中复杂叙事幽默的能力。我们的结果表明，即使是最先进的模型在该任务上仍落后于人类表现。我们的发现为AI在理解人类创造性表达方面的当前限制和潜在改进提供了见解。
英文摘要：
Recent advancements in large multimodal language models have demonstrated remarkable proficiency across a wide range of tasks. Yet, these models still struggle with understanding the nuances of human humor through juxtaposition, particularly when it involves nonlinear narratives that underpin many jokes and humor cues. This paper investigates this challenge by focusing on comics with contradictory narratives, where each comic consists of two panels that create a humorous contradiction. We introduce the YesBut benchmark, which comprises tasks of varying difficulty aimed at assessing AI's capabilities in recognizing and interpreting these comics, ranging from literal content comprehension to deep narrative reasoning. Through extensive experimentation and analysis of recent commercial or open-sourced large (vision) language models, we assess their capability to comprehend the complex interplay of the narrative humor inherent in these comics. Our results show that even state-of-the-art models still lag behind human performance on this task. Our findings offer insights into the current limitations and potential improvements for AI in understanding human creative expressions.
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。
