Papers by Rongzhou Bao
Defending Pre-trained Language Models from Adversarial Word Substitution Without Performance Sacrifice (2021.findings-acl)
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| Challenge: | Existing defense approaches suffer from notable performance loss and complexities. |
| Approach: | They propose a framework for detecting anomalies with frequency-aware randomization to defend adversarial word substitution. |
| Outcome: | The proposed framework outperforms existing defense methods over various tasks with much higher inference speed. |
Span Fine-tuning for Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing methods to fine-tune pre-trained language models are time-consuming and lack flexibility. |
| Approach: | They propose a span fine-tuning method which allows for a more efficient and efficient way of incorporating span-level information into pre-training. |
| Outcome: | Experiments on GLUE benchmark show that the proposed method significantly enhances the PrLM and offers more flexibility in an efficient way. |
Distinguishing Non-natural from Natural Adversarial Samples for More Robust Pre-trained Language Model (2022.findings-acl)
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| Challenge: | Recent studies on adversarial attacks achieve high success rates against PrLMs, claiming that they are not robust. |
| Approach: | They propose to use anomaly detector to evaluate PrLMs with more natural adversarial samples to evaluate their robustness. |
| Outcome: | The proposed method can be used to defend all types of attacks and achieve higher accuracy on adversarial samples and compliant samples than other defense frameworks. |
OneRec-Think: In-Text Reasoning for Generative Recommendation (2026.acl-long)
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Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang, Wuchao Li, PengFei Zheng, Xiangyu Wu, Yifei Hu, Qigen Hu, Xinchen Luo, Lejian Ren, Zhang Zixing, Qianqian Wang, Kuo Cai, Yunfan Wu, Hongtao Cheng, Zexuan Cheng, Lu Ren, Huanjie Wang, Yi Su, Ruiming Tang, Kun Gai, Guorui Zhou
| Challenge: | Existing generative models lack the capacity for explicit and controllable reasoning, a key advantage of LLMs. |
| Approach: | They propose a framework that integrates dialogue, reasoning, and personalized recommendation. |
| Outcome: | Experiments across public benchmarks show state-of-the-art performance. |