Papers by Zhe Niu
A Hong Kong Sign Language Corpus Collected from Sign-interpreted TV News (2024.lrec-main)
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| Challenge: | a new dataset is being developed to enrich resources for sign language research . the dataset is 16.07 hours of sign videos of two signers with a vocabulary of 6,515 glosses and 2,850 Chinese characters or 18K Chinese words. |
| Approach: | They introduce a new Hong Kong sign language dataset called TVB-HKSL-News . the dataset is collected from a TV news program and contains sign videos . they aim to support research in sign language recognition and translation . |
| Outcome: | The proposed dataset supports sign language recognition and translation research in Hong Kong . it consists of 16.07 hours of sign videos of two signers with a vocabulary of 6,515 glosses and 2,850 Chinese characters or 18K Chinese words . |
Privacy Risks of Intermediate Representations: Attribute Inference in Distributed LLM Inference (2026.findings-acl)
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| Challenge: | Distributed LLMs avoid raw inputs by transmitting intermediate hidden states, a practice widely assumed to preserve privacy. |
| Approach: | They propose a distributed inference framework that transmits intermediate hidden states to avoid sending raw inputs by exposing sensitive user attributes. |
| Outcome: | The proposed approach achieves Top-1 accuracy of 0.997 on CMS, 0.980 on Skytrax, and 0.986 on ECHR. |
When Does Language Matter? Multilingual Instructions Reveal Step-wise Language Sensitivity in Vision-Language-Action Models (2026.acl-long)
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| Challenge: | Vision-Language-Action models have shown strong performance in language-conditioned robotic manipulation, yet their robustness to linguistic variation remains poorly understood. |
| Approach: | They propose a step-wise inference-time intervention that aligns representations according to step language sensitivity, significantly improving performance under linguistic variation. |
| Outcome: | The proposed model significantly improves performance under linguistic variation under non-English instructions under language-agnostic steps. |
Fine-tuning LLMs with Cross-Attention-based Weight Decay for Bias Mitigation (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) excel in natural language processing tasks but often propagate societal biases from their training data, leading to discriminatory outputs. |
| Approach: | They propose a method that modifies the LLM architecture to mitigate bias by adjusting the attention weights of sensitive tokens. |
| Outcome: | The proposed method can handle multiple sensitive attributes and does not require full knowledge of sensitive tokens presented in the dataset. |
iPET: An Interactive Emotional Companion Dialogue System with LLM-Powered Virtual Pet World Simulation (2025.acl-demo)
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Zheyong Xie, Shaosheng Cao, Zuozhu Liu, Zheyu Ye, Zihan Niu, Chonggang Lu, Tong Xu, Enhong Chen, Zhe Xu, Yao Hu, Wei Lu
| Challenge: | Existing approaches to role-playing emotional companion products lack sustained personalization and contextual adaptability, limiting their effectiveness in real-world settings. |
| Approach: | They propose a virtual pet agent that can enhance user engagement through rich, dynamic pet behaviors and interactions tailored to individual preferences. |
| Outcome: | The proposed system has been deployed in a real-world, non-commercial product for 200 days and has demonstrated its effectiveness in practical applications. |
Fair RAG: End-to-End Fairness Across Retrieval and Generation (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) can amplify demographic bias by generating skewed context . prior work treats fairness in retrieval or generation in isolation, leaving end-to-end fairness underexplored . |
| Approach: | They propose a pipeline that jointly controls both retrieval and generation stages . large language models can handle a broad set of inference tasks, they argue . |
| Outcome: | The proposed pipeline reduces retriever-side skew and achieves lowest generator-side disparity while preserving utility. |