低资源微调大模型可能暗藏安全与公平性隐患，这项研究系统揭示了PEFT的对齐风险。
arXiv:2511.00382v2 Announce Type: replace Abstract: Organizations increasingly adapt Large Language Models (LLMs) from public repositories such as Hug…
多策略LLM训练新思路：用自动化任务排序取代单一LoRA，让各部件协同超越整体。
arXiv:2607.29601v1 Announce Type: new Abstract: Parameter-Efficient Fine-Tuning (PEFT) commonly adapts large language models using a single shared Low…
融合LoRA与Soft Prompting优势，用强化学习机制微调多模态大模型的新方案ART。
arXiv:2606.11854v1 Announce Type: cross Abstract: There are two main Parameter-Efficient Fine-Tuning (PEFT) techniques for Large Language Models (LLMs…
多任务微调新方法，用连续提示优化实现参数高效的大模型适配，减少数据需求。
arXiv:2605.14055v1 Announce Type: cross Abstract: Parameter-Efficient Fine-Tuning (PEFT) is widely used for adapting Large Language Models (LLMs) for …
新微调方法PreFT仅优化预填充阶段，解决大模型个性化服务时PEFT导致的吞吐量瓶颈，理论与实证兼备。
arXiv:2605.14217v1 Announce Type: cross Abstract: Large language models can now be personalised efficiently at scale using parameter efficient finetun…