Papers by Yushuo Chen
Learning to Imagine: Visually-Augmented Natural Language Generation (2023.acl-long)
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| Challenge: | Existing methods for natural language generation are pre-trained on text-only corpora, resulting in visual commonsense. |
| Approach: | They propose a method that makes pre-trained language models learn to imagine for visually-augmented natural language generation. |
| Outcome: | The proposed method is compatible with Transformer-based architecture. |
VidCapBench: A Comprehensive Benchmark of Video Captioning for Controllable Text-to-Video Generation (2025.findings-acl)
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Xinlong Chen, Yuanxing Zhang, Chongling Rao, Yushuo Guan, Jiaheng Liu, Fuzheng Zhang, Chengru Song, Qiang Liu, Di Zhang, Tieniu Tan
| Challenge: | Existing studies have not identified a link between video caption evaluation and T2V generation. |
| Approach: | They propose a video caption evaluation scheme specifically designed for T2V generation that integrates video annotation with caption evaluation. |
| Outcome: | The proposed system is agnostic to any particular caption format and can be used for training. |
Enhancing Scalability of Pre-trained Language Models via Efficient Parameter Sharing (2023.findings-emnlp)
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| Challenge: | Existing approaches to scale pre-trained language models to a deeper model depth share all parameters or use extra blocks. |
| Approach: | They propose a parameter-efficient approach to scaling pre-trained language models to a deeper model depth using matrix product operator. |
| Outcome: | The proposed model scales pre-trained language models to a deeper model depth by 4x and achieves 0.1 points higher than BERT-large for GLUE score. |
LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)
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Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, Zican Dong, Xia Chunxuan, Junyi Li, Kun Zhou, Xin Zhao, Ji-Rong Wen
| Challenge: | a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented. |
| Approach: | They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs). |
| Outcome: | The proposed library is based on extensive experiments in a variety of evaluation settings. |