3 citations · 3 across the 6 of their papers we have counts for
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cs.CL2024
Separate the Wheat from the Chaff: A Post-Hoc Approach to Safety Re-Alignment for Fine-Tuned Language Models
Di Wu, Xin Lu, Yanyan Zhao +1
Although large language models (LLMs) achieve effective safety alignment at the time of release, they still face various safety challenges. A key issue is that fine-tuning often co…
cs.CL2023★ 3 cited
A Split-and-Privatize Framework for Large Language Model Fine-Tuning
Xicong Shen, Yang Liu, Huiqi Liu +6
Fine-tuning is a prominent technique to adapt a pre-trained language model to downstream scenarios. In parameter-efficient fine-tuning, only a small subset of modules are trained o…