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cs.CL2026

When New Generators Arrive: Lifelong Machine-Generated Text Attribution via Ridge Feature Transfer

Zhen Sun, Yifan Liao, Zhicong Huang +4

Machine-generated text (MGT) attribution aims to identify the specific generator responsible for a given text, thereby providing fine-grained evidence for model accountability and…

cs.CL2026

Hidden Ads: Behavior Triggered Semantic Backdoors for Advertisement Injection in Vision Language Models

Duanyi Yao, Changyue Li, Zhicong Huang +2

Vision-Language Models (VLMs) are increasingly deployed in consumer applications where users seek recommendations about products, dining, and services. We introduce Hidden Ads, a n…

cs.CL2026

FIT to Forget: Robust Continual Unlearning for Large Language Models

Xiaoyu Xu, Minxin Du, Kun Fang +5

While large language models (LLMs) exhibit remarkable capabilities, they increasingly face demands to unlearn memorized privacy-sensitive, copyrighted, or harmful content. Existing…

cs.CL2025

EnchTable: Unified Safety Alignment Transfer in Fine-tuned Large Language Models

Jialin Wu, Kecen Li, Zhicong Huang +3

Many machine learning models are fine-tuned from large language models (LLMs) to achieve high performance in specialized domains like code generation, biomedical analysis, and math…

cs.CL2025

Understanding the Dilemma of Unlearning for Large Language Models

Qingjie Zhang, Haoting Qian, Zhicong Huang +5

Unlearning seeks to remove specific knowledge from large language models (LLMs), but its effectiveness remains contested. On one side, "forgotten" knowledge can often be recovered…