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cs.CL2025
DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression
Wei Huang, Huang Wei, Yinggui Wang
Large language models (LLMs) excel in general tasks but struggle with domain-specific ones, requiring fine-tuning with specific data. With many open-source LLMs available, selectin…
cs.CL2025
GradOT: Training-free Gradient-preserving Offsite-tuning for Large Language Models
Kai Yao, Zhaorui Tan, Penglei Gao +7
The rapid growth of large language models (LLMs) with traditional centralized fine-tuning emerges as a key technique for adapting these models to domain-specific challenges, yieldi…
cs.CL2025
PRIV-QA: Privacy-Preserving Question Answering for Cloud Large Language Models
Guangwei Li, Yuansen Zhang, Yinggui Wang +3
The rapid development of large language models (LLMs) is redefining the landscape of human-computer interaction, and their integration into various user-service applications is bec…