GODBench：面向视频评论艺术的多模态大语言模型基准
GODBench: A Benchmark for Multimodal Large Language Models in Video Comment Art
AI总结：
针对现有基准难以评估多模态大语言模型视频评论艺术创造力的问题，研究提出整合视频文本的GODBench基准，以及受物理波传播启发的RoT多步推理框架，实验证明该框架可有效提升模型创意创作能力。
摘要：
视频评论艺术通过提供传递幽默、讽刺或情感共鸣的创意内容提升用户参与度，这要求对文化与语境的细微差别具备细腻且全面的理解。尽管多模态大语言模型（MLLMs）和思维链（CoT）已在STEM任务（如数学和编程）中展现出强大的推理能力，但它们在生成有共鸣的笑话、有见地的讽刺等创意表达方面仍显吃力。此外，现有基准受限于模态有限、类别不足，阻碍了对基于视频的评论艺术创作中综合创造力的探索。为解决这些局限，我们提出GODBench——一个整合视频与文本模态的新型基准，用于系统评估MLLMs创作评论艺术的能力。此外，受物理学中波的传播模式启发，我们提出思维涟漪（Ripple of Thought, RoT），这是一个旨在提升MLLMs创造力的多步推理框架。大量实验表明，现有MLLMs和CoT方法在理解和生成创意视频评论方面仍面临显著挑战。相比之下，RoT为提升创意创作提供了有效途径，凸显了其推动基于MLLM的创造力取得实质性进展的潜力。GODBench已在https://github.com/stan-lei/GODBench-ACL2025公开发布。
英文摘要：
Video Comment Art enhances user engagement by providing creative content that conveys humor, satire, or emotional resonance, requiring a nuanced and comprehensive grasp of cultural and contextual subtleties. Although Multimodal Large Language Models (MLLMs) and Chain-of-Thought (CoT) have demonstrated strong reasoning abilities in STEM tasks (e.g. mathematics and coding), they still struggle to generate creative expressions such as resonant jokes and insightful satire. Moreover, existing benchmarks are constrained by their limited modalities and insufficient categories, hindering the exploration of comprehensive creativity in video-based Comment Art creation. To address these limitations, we introduce GODBench, a novel benchmark that integrates video and text modalities to systematically evaluate MLLMs' abilities to compose Comment Art. Furthermore, inspired by the propagation patterns of waves in physics, we propose Ripple of Thought (RoT), a multi-step reasoning framework designed to enhance the creativity of MLLMs. Extensive experiments reveal that existing MLLMs and CoT methods still face significant challenges in understanding and generating creative video comments. In contrast, RoT provides an effective approach to improve creative composing, highlighting its potential to drive meaningful advancements in MLLM-based creativity. GODBench is publicly available at https://github.com/stan-lei/GODBench-ACL2025.
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。
