Papers by Lingjuan Lyu
GNN-SL: Sequence Labeling Based on Nearest Examples via GNN (2023.findings-acl)
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| Challenge: | Existing sequence labeling algorithms can be decomposed into two parts . |
| Approach: | They propose a graph neural networks sequence labeling (GNN-SL) that augments the vanilla SL model output with similar tagging examples retrieved from the whole training set. |
| Outcome: | The proposed model performs well on three sequence labeling tasks. |
Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models (2022.findings-emnlp)
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| Challenge: | Existing methods for defending NLP models against backdoors have ignored the clean weights of PLMs. |
| Approach: | They exploit pre-trained weights to mitigate backdoors in fine-tuned NLP models . they use a fine-mixing technique and an Embedding Purification technique to do the same . |
| Outcome: | The proposed method outperforms baseline mitigation methods on three single-sentence sentiment classification tasks and two sentence-pair classification tasks. |
Defending against Indirect Prompt Injection by Instruction Detection (2025.findings-emnlp)
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Tongyu Wen, Chenglong Wang, Xiyuan Yang, Haoyu Tang, Yueqi Xie, Lingjuan Lyu, Zhicheng Dou, Fangzhao Wu
| Challenge: | Indirect Prompt Injection attacks can be exploited by LLMs that are embedded with external data. |
| Approach: | They propose a detection-based approach that leverages the behavioral states of LLMs to identify potential IPI attacks. |
| Outcome: | The proposed approach reduces the success rate of attacks to 0.03% on the BIPIA benchmark. |
Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness (2020.findings-emnlp)
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| Challenge: | Existing approaches to learn text representations can encode private information of the input, thus can be exploited to recover such information with reasonable accuracy. |
| Approach: | They propose a novel approach to preserve privacy of the extracted representation from text by combining differential privacy with dropout. |
| Outcome: | The proposed approach preserves privacy of the extracted representation from text while masking words via dropout can enhance privacy. |
Extracted BERT Model Leaks More Information than You Think! (2022.emnlp-main)
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| Challenge: | Existing pre-trained language models are vulnerable to model extraction attacks . model extraction can cause severe privacy leakage even when victim models are facilitated with state-of-the-art defensive strategies. |
| Approach: | They propose to launch an attribute-inference attack against an extracted BERT model to prevent privacy leakage. |
| Outcome: | The proposed attack can cause severe privacy leakage even when victim models are facilitated with state-of-the-art defensive strategies. |
Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs (2022.coling-1)
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| Challenge: | Existing MLaaS models are vulnerable to imitation attacks, but none of the stolen models can outperform the original black-box APIs. |
| Approach: | They conduct unsupervised domain adaptation and multi-victim ensemble to show attackers could surpass victims. |
| Outcome: | The proposed model outperforms the original black-box models on transferred domains. |
Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark (2023.acl-long)
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Wenjun Peng, Jingwei Yi, Fangzhao Wu, Shangxi Wu, Bin Bin Zhu, Lingjuan Lyu, Binxing Jiao, Tong Xu, Guangzhong Sun, Xing Xie
| Challenge: | Large language models (LLMs) have demonstrated exceptional abilities in both text understanding and generation. |
| Approach: | They propose an Embedding Watermark method that implants backdoors on embeddings to protect copyright of large language models. |
| Outcome: | The proposed method protects the copyright of large language models without compromising service quality while minimizing the adverse impact on the original embeddings’ utility. |
Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning (2024.findings-naacl)
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| Challenge: | Existing methods for parameter-efficient fine-tuning (PEFT) are not effective for weight-poisoning backdoor attacks. |
| Approach: | They propose a parameter-efficient fine-tuning (PEFT) method that updates only a limited set of model parameters and provides a robust defense against weight-poisoning backdoor attacks. |
| Outcome: | The proposed method identifies poisoned samples through confidence and is robust against weight-poisoning backdoor attacks. |
Model Extraction and Adversarial Transferability, Your BERT is Vulnerable! (2021.naacl-main)
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| Challenge: | Pretrained language models are used for natural language processing (NLP) but when they are deployed as a service, they can suffer from different attacks . |
| Approach: | They propose two defence strategies to protect the target model from adversarial attacks . they show that model extraction and adversarially transferable attacks can be effective . |
| Outcome: | The extracted model can lead to highly transferable adversarial attacks against the target model. |