@inproceedings{lei-etal-2025-godbench,
title = "{GODB}ench: A Benchmark for Multimodal Large Language Models in Video Comment Art",
author = "Lei, Yiming and
Zhang, Chenkai and
Liu, Zeming and
Leng, Haitao and
Liu, ShaoGuo and
Gao, Tingting and
Liu, Qingjie and
Wang, Yunhong",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.583/",
doi = "10.18653/v1/2025.acl-long.583",
pages = "11884--11952",
ISBN = "979-8-89176-251-0",
abstract = "***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 on GODBench 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 improving creative composing, highlighting its potential to drive meaningful advancements in MLLM-based creativity."
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<abstract>***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 on GODBench 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 improving creative composing, highlighting its potential to drive meaningful advancements in MLLM-based creativity.</abstract>
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%0 Conference Proceedings
%T GODBench: A Benchmark for Multimodal Large Language Models in Video Comment Art
%A Lei, Yiming
%A Zhang, Chenkai
%A Liu, Zeming
%A Leng, Haitao
%A Liu, ShaoGuo
%A Gao, Tingting
%A Liu, Qingjie
%A Wang, Yunhong
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F lei-etal-2025-godbench
%X ***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 on GODBench 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 improving creative composing, highlighting its potential to drive meaningful advancements in MLLM-based creativity.
%R 10.18653/v1/2025.acl-long.583
%U https://aclanthology.org/2025.acl-long.583/
%U https://doi.org/10.18653/v1/2025.acl-long.583
%P 11884-11952
Markdown (Informal)
[GODBench: A Benchmark for Multimodal Large Language Models in Video Comment Art](https://aclanthology.org/2025.acl-long.583/) (Lei et al., ACL 2025)
ACL
- Yiming Lei, Chenkai Zhang, Zeming Liu, Haitao Leng, ShaoGuo Liu, Tingting Gao, Qingjie Liu, and Yunhong Wang. 2025. GODBench: A Benchmark for Multimodal Large Language Models in Video Comment Art. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11884–11952, Vienna, Austria. Association for Computational Linguistics.