Papers by Quanquan Gu
Large Language Models Can Be Contextual Privacy Protection Learners (2024.emnlp-main)
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Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable linguistic comprehension and generation capability, but when applied to specialized industries, they face challenges such as hallucination, insufficient domain knowledge, and failing to incorporate the latest domain knowledge. |
| Approach: | They propose a paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy. |
| Outcome: | The proposed model protects private data while enhancing the model's knowledge. |
Variance-reduced First-order Meta-learning for Natural Language Processing Tasks (2021.naacl-main)
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| Challenge: | Existing studies show that meta-learning can overfit to some specific adaptation when we have heterogeneous tasks. |
| Approach: | They propose to reduce the variance of the gradient estimator used in task adaptation by adding a new variance reduction term to the gradient estimation. |
| Outcome: | Experiments on few-shot text classification and multi-domain dialog state tracking show that the proposed method outperforms existing methods. |