Papers by Zhengdong Lu
PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restoration (2025.acl-long)
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| Challenge: | Existing privacy protection methods for large language models suffer from performance degradation or large inference time overhead. |
| Approach: | They propose a plug-and-play method to protect the privacy of user inputs during LLM inference . they use offline restoration vectors to train restoration vector for each privacy span type . |
| Outcome: | The proposed method can prevent the linear growth of the privacy budget. |
A Prism Module for Semantic Disentanglement in Name Entity Recognition (P19-1)
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| Challenge: | Xu et al., 2015) proposed a noise reduction mechanism to disentangle semantics of words . hard and soft attention mechanisms are used to reduce noise in NLP tasks . |
| Approach: | They propose a prism module to disentangle semantic aspects of words and reduce noise . they propose combining prism modules with downstream models to improve model performance . |
| Outcome: | The proposed method significantly improves the performance of baselines on named entity recognition (NER) tasks. |
Zero-shot Event Detection Using a Textual Entailment Model as an Enhanced Annotator (2024.lrec-main)
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| Challenge: | Recent work proposed to use a pre-trained textual entailment model for event detection . but, those methods treated the TE model as a frozen annotator . |
| Approach: | They propose to use TE models to annotate large-scale unlabeled text and annotated data to fine-tune the TE model. |
| Outcome: | The proposed method outperforms baseline methods by 15% on the ACE05 dataset. |
Object-oriented Neural Programming (OONP) for Document Understanding (P18-1)
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| Challenge: | Object-oriented Neural Programming (OONP) is a framework for semantically parsing documents in domains. |
| Approach: | They propose a framework for semantically parsing documents in specific domains using OONP . OOPN parsers use a rich family of operations to represent the semantics of the document . |
| Outcome: | The proposed framework can learn to handle fairly complicated ontology with training data of modest sizes. |