Papers by Yu-Xiang Wang
Provably Confidential Language Modelling (2022.naacl-main)
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| Challenge: | Existing methods to train language models without memorizing sensitive data are mismatched and can be difficult to screen and filter. |
| Approach: | They propose a method to train language generation models while protecting the confidential segments of training data. |
| Outcome: | The proposed method prevents unintended memorization by randomizing parts of the training process while protecting strong confidentiality. |
Efficiently Identifying Watermarked Segments in Mixed-Source Texts (2025.acl-long)
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| Challenge: | Existing methods for watermarking entire documents neglect identifying individual watermark segments within long, mixed-source documents. |
| Approach: | They propose a framework for partial watermark detection that detects whether there is a watermark segment in long text and an adaptive online learning algorithm to pinpoint the precise location of watermark segments. |
| Outcome: | The proposed framework outperforms existing methods and is adaptable to other watermarking techniques. |
Distillation-Resistant Watermarking for Model Protection in NLP (2022.findings-emnlp)
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| Challenge: | Existing protection methods such as watermarking only work for images but are not applicable to text. |
| Approach: | They propose a technique that injects watermarks into the victim’s prediction probability corresponding to a secret key and is able to detect such a key by probing a suspect model. |
| Outcome: | The proposed technique detects stealing suspects at 100% accuracy on four NLP tasks while the prior method fails on two. |