AI 大模型

语言大模型 / LLM

大语言模型、预训练、指令微调、后训练和语言模型应用。

2025-12-11 至 2025-12-11 共收录 2 篇信号源：cs.CL, cs.AI, cs.LG

1. 后训练与偏好优化 2 篇

2508.06783 2025-12-11 cs.LG cs.AI cs.CR cs.IT math.IT 91%

PROPS: Progressively Private Self-alignment of Large Language Models

PROPS: 大型语言模型的逐步隐私自对齐

Noel Teku, Fengwei Tian, Payel Bhattacharjee, Souradip Chakraborty, Amrit Singh Bedi, Ravi Tandon

专题命中后训练与偏好优化：large language model(title,abstract)；language model(title,abstract)；LLM(abstract)；RLHF(abstract)

AI总结 PROPS通过多阶段隐私保护对齐框架，在保护偏好标签隐私的同时提升LLM对齐效果，实现更高的胜率。

Comments Accepted in the Transactions on Machine Learning Research (TMLR), 2025

Journal ref Transactions on ML Research (TMLR) 2025

2512.09485 2025-12-11 cs.CR cs.AI 83%

Advancing LLM-Based Security Automation with Customized Group Relative Policy Optimization for Zero-Touch Networks

通过定制化群体相对策略优化推进基于大语言模型的安全自动化以实现零接触网络

Xinye Cao, Yihan Lin, Guoshun Nan, Qinchuan Zhou, Yuhang Luo, Yurui Gao, Zeliang Zhang, Haolang Lu, Qimei Cui, Yanzhao Hou, Xiaofeng Tao, Tony Q. S. Quek

机构 * National Engineering Research Center for Mobile Network Technologies, Beijing University of Posts and Telecommunications（移动网络技术国家工程研究中心，北京邮电大学）； Beijing University of Posts and Telecommunications（北京邮电大学）； Singapore University of Technology and Design（新加坡科技设计大学）； Department of Electronic Engineering, Kyung Hee University（韩国庆熙大学电子工程系）

专题命中后训练与偏好优化：LLM(title)；large language model(abstract)；language model(abstract)；分类 cs.AI

AI总结本文提出SecLoop和SA-GRPO，通过定制化群体相对策略优化，解决6G零接触网络中的安全自动化挑战。

Comments Accepted by IEEE JSAC. This work has been submitted to the IEEE for possible publication