Papers by Xuedong Wang
Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization (2023.acl-long)
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Pengcheng He, Baolin Peng, Song Wang, Yang Liu, Ruochen Xu, Hany Hassan, Yu Shi, Chenguang Zhu, Wayne Xiong, Michael Zeng, Jianfeng Gao, Xuedong Huang
| Challenge: | Z-Code++ is a pre-trained language model optimized for abstractive text summarization. |
| Approach: | They propose a pre-trained language model optimized for abstractive text summarization that uses a two-phase pre-training technique to improve model's performance. |
| Outcome: | The proposed model outperforms the competing models on low-resource summarization tasks in zero-shot and few-shot settings. |
Crossroads of Optimization under Uncertainty: How to Choose the Optimal Model (2026.findings-acl)
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| Challenge: | Existing approaches to Optimization under Uncertainty (OuU) have inherent limitations and advantages. |
| Approach: | They propose a framework that automates the modeling and solving of six types of uncertainty models and generates mapping pairs to explore the potential relationship between optimization problems and optimal models. |
| Outcome: | The proposed framework achieves superior performance even on specific model types, with correlation analysis showing that data scale and specific scenario significantly influence model selection. |