语言大模型 / LLM

Best Practices for Large Language Models in Radiology

Christian Bluethgen, Dave Van Veen, Cyril Zakka, Katherine Link, Aaron Fanous, Roxana Daneshjou, Thomas Frauenfelder, Curtis Langlotz, Sergios Gatidis, Akshay Chaudhari

机构 * Stanford Center for Artificial Intelligence in Medicine and Imaging（斯坦福大学人工智能医学与成像中心） ； Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich（苏黎世大学医院放射诊断与介入放射学） ； Department of Electrical Engineering, Stanford University（斯坦福大学电气工程系） ； Department of Cardiothoracic Surgery, Stanford Medicine（斯坦福医学院心胸外科系） ； Hugging Face ； Department of Medical Education, Icahn School of Medicine at Mount Sinai（伊坎医学院Mount Sinai医学教育系） ； NVIDIA Corporation（NVIDIA公司） ； UT Health San Antonio（UT健康科学中心圣安东尼奥分校） ； Department of Dermatology, Redwood City, CA, USA（红木城加州大学皮肤病学系） ； Department of Biomedical Data Science, Stanford, CA, USA（斯坦福大学生物医学数据科学系） ； Department of Medicine, Stanford, CA, USA（斯坦福大学医学系） ； Department of Radiology, Stanford University（斯坦福大学放射学系） 纠错

专题命中 指令微调 ：large language model(title,abstract)；language model(title,abstract)；LLM(abstract)；prompting(abstract)

Comments A redacted version of this preprint has been accepted for publication in Radiology

Journal ref Radiology 2025

Can adversarial attacks by large language models be attributed?

Manuel Cebrian, Andres Abeliuk, Jan Arne Telle

机构 * Center for Automation and Robotics, Spanish National Research Council（自动化与机器人中心，西班牙国家研究理事会） ； Department of Computer Science, University of Chile（计算机科学系，智利大学） ； Department of Informatics, University of Bergen（信息学系，卑尔根大学） 纠错

专题命中 指令微调 ：large language model(title,abstract)；language model(title,abstract)；LLM(abstract)；分类 cs.CL、cs.AI

Comments 22 pages, 5 figures, 2 tables

FineMedLM-o1: Enhancing Medical Knowledge Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Training

Hongzhou Yu, Tianhao Cheng, Yingwen Wang, Wen He, Qing Wang, Ying Cheng, Yuejie Zhang, Rui Feng, Xiaobo Zhang

机构 * Fudan University（复旦大学） ； Children’s Hospital of Fudan University（复旦大学附属儿童医院） 纠错

专题命中 指令微调 ：LLM(title)；large language model(abstract)；language model(abstract)；SFT(abstract)

Visual Language Models as Zero-Shot Deepfake Detectors

Viacheslav Pirogov

专题命中 指令微调 ：language model(title,abstract)；分类 cs.AI、cs.LG；foundation model(comments)

Comments Accepted to the ICML 2025 Workshop on Reliable and Responsible Foundation Models

CollabLLM: From Passive Responders to Active Collaborators

Shirley Wu, Michel Galley, Baolin Peng, Hao Cheng, Gavin Li, Yao Dou, Weixin Cai, James Zou, Jure Leskovec, Jianfeng Gao

机构 * stanford（斯坦福大学） ； microsoft（微软公司） 纠错

专题命中 指令微调 ：LLM(abstract)；large language model(abstract)；language model(abstract)；分类 cs.AI

Comments Outstanding Paper Award at ICML 2025

Don't Lag, RAG: Training-Free Adversarial Detection Using RAG

Roie Kazoom, Raz Lapid, Moshe Sipper, Ofer Hadar

机构 * Electrical and Computer Engineering, Ben Gurion University, Beer Sheba 84105, Israel（电子与计算机工程系，本· Gurion 大学） ； Computer Science, Ben Gurion University, Beer Sheba 84105, Israel（计算机科学系，本· Gurion 大学） 纠错

专题命中 指令微调 ：language model(abstract)；分类 cs.AI、cs.LG

Comments Accepted at VecDB @ ICML 2025

Human-Level Competitive Pokémon via Scalable Offline Reinforcement Learning with Transformers

Jake Grigsby, Yuqi Xie, Justin Sasek, Steven Zheng, Yuke Zhu

专题命中 指令微调 ：LLM(abstract)；分类 cs.LG

Comments Reinforcement Learning Conference 2025

See Different, Think Better: Visual Variations Mitigating Hallucinations in LVLMs

Ziyun Dai, Xiaoqiang Li, Shaohua Zhang, Yuanchen Wu, Jide Li

专题命中 指令微调 ：language model(abstract)

Comments Accepted by ACM MM25

Journal ref 33rd ACM International Conference on Multimedia 2025