Papers by Wenkai Li
User Perceptions vs. Proxy LLM Judges: Privacy and Helpfulness in LLM Responses to Privacy-Sensitive Scenarios (2026.acl-long)
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| Challenge: | Large language models (LLMs) are rapidly being adopted for tasks like drafting emails, summarizing meetings, and answering health questions. |
| Approach: | They conducted a scenario-based evaluation of Large language models (LLMs) using 90 PrivacyLens scenarios. |
| Outcome: | The proposed models can leak private information in complex scenarios, but they do not measure user perceptions directly. |
Automatic Generation of Model and Data Cards: A Step Towards Responsible AI (2024.naacl-long)
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| Challenge: | Existing models and datasets are incomplete and lack consistent documentation. |
| Approach: | They propose an automated generation approach using Large Language Models (LLMs) their paper establishes a comprehensive dataset and develops 'CardGen' pipeline . |
| Outcome: | The proposed approach exhibits enhanced completeness, objectivity, and faithfulness in generated model and data cards, a significant step in responsible AI documentation practices ensuring better accountability and traceability. |
Toward Global AI Inclusivity: A Large-Scale Multilingual Terminology Dataset (GIST) (2025.findings-acl)
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Jiarui Liu, Iman Ouzzani, Wenkai Li, Lechen Zhang, Tianyue Ou, Houda Bouamor, Zhijing Jin, Mona T. Diab
| Challenge: | Despite advances in machine translation, domain-specific terminology translation remains challenging. |
| Approach: | They propose a large-scale multilingual AI terminology dataset that combines LLMs for extraction with human expertise for translation. |
| Outcome: | The proposed framework combines human translation expertise with LLMs to improve translation accuracy and improve BLEU and COMET scores. |
BIG5-CHAT: Shaping LLM Personalities Through Training on Human-Grounded Data (2025.acl-long)
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| Challenge: | Existing methods for embedding human personality traits into LLMs are limited by realism and validity issues. |
| Approach: | They propose to use a large-scale dataset to embed human personality traits into LLMs . they use supervised fine-tuning and direct preference optimization to train LLM models . |
| Outcome: | The proposed methods outperform prompting on personality assessments and IPIP-NEO, and show higher conscientiousness, agreeableness, lower extraversion, and lower neuroticism on reasoning tasks. |
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter (2023.findings-acl)
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| Challenge: | Existing frameworks for federated multilingual neural machine translation (Fed-MNMT) are limited in language resources. |
| Approach: | They propose a framework that keeps PLMs frozen and only transfers lightweight adapter modules between clients. |
| Outcome: | The proposed framework reduces communication cost by over 98% while achieving similar or even better performance compared to baselines. |
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models (2021.naacl-main)
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| Challenge: | Recent studies reveal a security threat to natural language processing models, called the Backdoor Attack. |
| Approach: | They propose to hack a model by modifying one single word embedding vector without sacrificing accuracy on clean samples. |
| Outcome: | The proposed method is more efficient and stealthier on sentiment analysis and sentence-pair classification tasks. |
Correct When Paired, Wrong When Split: Decoupling and Editing Modality-Specific Neurons in MLLMs (2026.acl-long)
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Tingchao Fu, Wenkai Wang, Fanxiao Li, Huadong Zhang, Jinhong Zhang, Dayang Li, Yunyun Dong, Renyang Liu, Wei Zhou
| Challenge: | Existing knowledge editing paradigms suffer from editing decoupling failures . entity knowledge is sequestered into disentangled modality-specific pathways . |
| Approach: | They propose a method that explicitly disentangles and localizes modality-specific neuron groups for targeted knowledge. |
| Outcome: | The proposed method outperforms baselines in reliability and consistency while preserving model locality. |
RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models (2021.emnlp-main)
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| Challenge: | Backdoor attacks are a serious threat to the safety of reusing deep neural networks (DNNs). |
| Approach: | They propose an efficient online defense mechanism based on robustness-aware perturbations to distinguish poisoned and clean samples to defend against backdoor attacks on natural language processing models. |
| Outcome: | The proposed method achieves better defending performance and lower computational costs than existing defense methods. |
Rethinking Stealthiness of Backdoor Attack against NLP Models (2021.acl-long)
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| Challenge: | Existing backdoor attacks are not stealthy to system deployers or users. |
| Approach: | They propose a novel backdoor attack method based on negative data augmentation and modifying word embeddings that is much stealthier while maintaining pretty good attacking performance. |
| Outcome: | The proposed method is much stealthier while maintaining pretty good attacking performance. |
Measure Twice, Click Once: Co-evolving Proposer and Visual Critic via Reinforcement Learning for GUI Grounding (2026.acl-long)
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| Challenge: | Graphical User Interface (GUI) grounding requires mapping natural language instructions to precise pixel coordinates due to visually homogeneous elements and dense layouts. |
| Approach: | They propose to replace static consistency strategies with a learnable selection mechanism that selects the optimal target by critiquing its own proposals rendered on the screenshot. |
| Outcome: | The proposed model significantly improves both grounding and critiquing capabilities over 6 benchmarks. |