Papers by Hengyu WU
SLIM: Stealthy Low-Coverage Black-Box Watermarking via Latent-Space Confusion Zones (2026.findings-acl)
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| Challenge: | Training data is a critical asset in Large Language Model (LLM) development and is often proprietary. |
| Approach: | They propose a framework that allows per-user data provenance verification under strict black-box access. |
| Outcome: | The proposed framework achieves ultra-low coverage capability, strong black-box verification performance, and great scalability while preserving both stealthiness and model utility. |
Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics (2026.acl-long)
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Ziwen Xu, Chenyan WU, Hengyu Sun, Haiwen Hong, Mengru Wang, Yunzhi Yao, Longtao Huang, Hui Xue, Shumin Deng, Zhixuan Chu, Huajun Chen, Ningyu Zhang
| Challenge: | Methods for controlling large language models (LLMs) are often studied in isolation, obscuring connections and making comparison difficult. |
| Approach: | They propose a preference-utility analysis that separates control effects into preference and utility, and measures both on a shared log-odds scale using polarity-paired contrastive examples. |
| Outcome: | The proposed approach improves preference while preserving utility. |