ShaoGuo Liu
2025
GODBench: A Benchmark for Multimodal Large Language Models in Video Comment Art
Yiming Lei
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Chenkai Zhang
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Zeming Liu
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Haitao Leng
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ShaoGuo Liu
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Tingting Gao
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Qingjie Liu
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Yunhong Wang
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
***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.
2022
CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning
Penghui Wei
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Xuanhua Yang
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ShaoGuo Liu
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Liang Wang
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Bo Zheng
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track
This paper focuses on automatically generating the text of an ad, and the goal is that the generated text can capture user interest for achieving higher click-through rate (CTR). We propose CREATER, a CTR-driven advertising text generation approach, to generate ad texts based on high-quality user reviews. To incorporate CTR objective, our model learns from online A/B test data with contrastive learning, which encourages the model to generate ad texts that obtain higher CTR. To make use of large-scale unpaired reviews, we design a customized self-supervised objective reducing the gap between pre-training and fine-tuning. Experiments on industrial datasets show that CREATER significantly outperforms current approaches. It has been deployed online in a leading advertising platform and brings uplift on core online metrics.
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- Tingting Gao 1
- Yiming Lei 1
- Haitao Leng 1
- Zeming Liu 1
- Qingjie Liu 1
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