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cs.CL2025
Learning from Reference Answers: Versatile Language Model Alignment without Binary Human Preference Data
Shuai Zhao, Yunqiu Xu, Linchao Zhu +1
Large language models~(LLMs) are expected to be helpful, harmless, and honest. In different alignment scenarios, such as safety, confidence, and general preference alignment, binar…
cs.CL2025
Protecting Copyrighted Material with Unique Identifiers in Large Language Model Training
Shuai Zhao, Linchao Zhu, Ruijie Quan +1
A primary concern regarding training large language models (LLMs) is whether they abuse copyrighted online text. With the increasing training data scale and the prevalence of LLMs…
cs.CL2024
FragRel: Exploiting Fragment-level Relations in the External Memory of Large Language Models
Xihang Yue, Linchao Zhu, Yi Yang
To process contexts with unlimited length using Large Language Models (LLMs), recent studies explore hierarchically managing the long text. Only several text fragments are taken fr…