Papers by Xuechen Li
Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe (2023.acl-long)
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Xiang Yue, Huseyin Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Hoda Shajari, Huan Sun, David Levitan, Robert Sim
| Challenge: | Privacy concerns have increased in data-driven products due to the tendency of machine learning models to memorize sensitive training data. |
| Approach: | They propose a method for generating useful synthetic text with a formal privacy guarantee by fine-tuning a pretrained generative language model with DP. |
| Outcome: | The proposed method produces synthetic text competitive in terms of utility with its non-private counterpart, while providing strong protection against potential privacy leakages. |
Improving Cross-lingual Transfer with Contrastive Negative Learning and Self-training (2024.lrec-main)
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| Challenge: | Recent studies improve cross-lingual transfer learning by better aligning the internal representations within the multilingual model or exploring the information of the target language using self-training. |
| Approach: | They propose to use negative pairs to align the multilingual model and self-train the model to converge on the obtained clean pseudo-labels. |
| Outcome: | The proposed method improves upon the baseline models and can serve as a beneficial complement to the alignment-based methods. |