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🤖 AI·大模型 arXiv AI 2026-07-07

MPSelectTune: Prompt-type Selection for Fine-tuning improves Concept Unlearning in LLMs

想给大模型精准“洗脑”忘掉有害概念？这项研究用提示类型选择优化微调，让概念遗忘更彻底，安全性拉满。

arXiv:2607.03932v1 Announce Type: cross Abstract: LLMs can be conveniently adapted to a diverse set of tasks, e.g, prediction, question-answering task…

大模型安全概念遗忘提示类型微调方法 llm对齐

📝 深度技术 arXiv AI 2026-06-16

G-Loss: Graph-Guided Fine-Tuning of Language Models

本论文提出G-Loss，一种利用图结构引导语言模型微调的新范式，为NLP与图学习交叉领域带来创新解决方案。

arXiv:2604.25853v3 Announce Type: replace-cross Abstract: Traditional loss functions, including cross-entropy, contrastive, triplet, and su pervised c…

g-loss 图引导微调语言模型图神经网络微调方法

📝 深度技术 arXiv AI 2026-05-29

NaRA: Noise-Aware LoRA for Parameter-Efficient Fine-Tuning of Diffusion LLMs

NaRA提出噪声感知LoRA，显著提升扩散大语言模型参数高效微调效果

arXiv:2605.29716v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising non-autoregressive generative para…

噪声感知lora 参数高效微调扩散llm 低秩适应微调方法

📅 日期

2026-05-20 2026-05-19

🐂 牛哥精选

MPSelectTune: Prompt-type Selection for Fine-tuning improves Concept Unlearning in LLMs

G-Loss: Graph-Guided Fine-Tuning of Language Models

NaRA: Noise-Aware LoRA for Parameter-Efficient Fine-Tuning of Diffusion LLMs

📅 日期