Papers by Leye Wang
A Unified Knowledge Graph Augmentation Service for Boosting Domain-specific NLP Tasks (2023.findings-acl)
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| Challenge: | Existing domain-specific pre-trained language models lack domain knowledge in domain-focused training. |
| Approach: | They propose a unified domain language model development service to inject domain knowledge into the PLM fine-tuning stage. |
| Outcome: | Experiments on domain-specific text classification and QA tasks verify the effectiveness and generalizability of KnowledgeDA. |
Your Inference Request Will Become a Black Box: Confidential Inference for Cloud-based Large Language Models (2026.acl-long)
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Chung-ju Huang, Huiqiang Zhao, Yuanpeng He, Lijian Li, Wenpin Jiao, Zhi Jin, Peixuan Chen, Leye Wang
| Challenge: | Existing approaches fail to ensure privacy, maintain model performance, and preserve computational efficiency simultaneously. |
| Approach: | They propose a confidential inference framework that partitions the LLM pipeline between a client-verified Confidential Virtual Machine (CVM) and the public cloud to protect client data without compromising the cloud’s model intellectual property or inference quality. |
| Outcome: | The proposed framework can defend against state-of-the-art token inference attacks while preserving model privacy, performance, and efficiency. |