语言大模型 / LLM

On Domain-Adaptive Post-Training for Multimodal Large Language Models

Daixuan Cheng, Shaohan Huang, Ziyu Zhu, Xintong Zhang, Wayne Xin Zhao, Zhongzhi Luan, Bo Dai, Zhenliang Zhang

机构 * BIGAI ； BUAA（北京航空航天大学） ； THU（清华大学） ； BIT（北京理工大学） ； RUC（中国人民大学） 纠错

专题命中 领域大模型 ：large language model(title,abstract)；language model(title,abstract)；post-training(title,abstract)；分类 cs.CL、cs.LG

Comments EMNLP 2025 Findings, Project Page: https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains

Constructing a Norm for Children's Scientific Drawing: Distribution Features Based on Semantic Similarity of Large Language Models

Yi Zhang, Fan Wei, Jingyi Li, Yan Wang, Yanyan Yu, Jianli Chen, Zipo Cai, Xinyu Liu, Wei Wang, Sensen Yao, Peng Wang, Zhong Wang

专题命中 领域大模型 ：large language model(title,abstract)；language model(title,abstract)；LLM(abstract)；分类 cs.CL、cs.AI

Journal ref biology methods and protocols, 2025, 10(01)

From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine

Lukas Buess, Matthias Keicher, Nassir Navab, Andreas Maier, Soroosh Tayebi Arasteh

专题命中 领域大模型 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.AI

Journal ref Biomed. Eng. Lett. 15 (2025)

X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models

Zeyi Sun, Ziyang Chu, Pan Zhang, Tong Wu, Xiaoyi Dong, Yuhang Zang, Yuanjun Xiong, Dahua Lin, Jiaqi Wang

机构 * Shanghai Jiao Tong University（上海交通大学） ； Shanghai AI Laboratory（上海人工智能实验室） ； Tsinghua University（清华大学） ； The Chinese University of Hong Kong（香港中文大学） ； CPII under InnoHK（创新香港科技促进会） ； MThreads AI 纠错

专题命中 领域大模型 ：foundation model(title)；large language model(abstract)；language model(abstract)；分类 cs.AI、cs.LG

Comments code: https://github.com/SunzeY/X-Prompt

HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Models

Aakash Tripathi, Asim Waqas, Matthew B. Schabath, Yasin Yilmaz, Ghulam Rasool

专题命中 领域大模型 ：large language model(abstract)；language model(abstract)；foundation model(abstract)；分类 cs.AI、cs.LG

Bridging Domain Gaps for Fine-Grained Moth Classification Through Expert-Informed Adaptation and Foundation Model Priors

Ross J Gardiner, Guillaume Mougeot, Sareh Rowlands, Benno I Simmons, Flemming Helsing, Toke Thomas Høye

机构 * University of Exeter（埃克塞特大学） ； Aarhus University（奥胡斯大学） 纠错

专题命中 领域大模型 ：foundation model(title,abstract)

MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning

Run-Ze Fan, Zengzhi Wang, Pengfei Liu

机构 * Shanghai Jiao Tong University（上海交通大学） ； SII ； GAIR Lab（GAIR实验室） 纠错

专题命中 领域大模型 ：post-training(title)；分类 cs.CL、cs.AI、cs.LG

Comments 39 pages; Github: https://github.com/GAIR-NLP/MegaScience; HF: https://huggingface.co/MegaScience

AI-Powered Detection of Inappropriate Language in Medical School Curricula

Chiman Salavati, Shannon Song, Scott A. Hale, Roberto E. Montenegro, Shiri Dori-Hacohen, Fabricio Murai

专题命中 领域大模型 ：language model(abstract)；small language model(abstract)；分类 cs.CL、cs.AI

Comments Accepted at 2025 AAAI/ACM AI, Ethics and Society Conference (AIES'25)

Inference Gap in Domain Expertise and Machine Intelligence in Named Entity Recognition: Creation of and Insights from a Substance Use-related Dataset

Sumon Kanti Dey, Jeanne M. Powell, Azra Ismail, Jeanmarie Perrone, Abeed Sarker

机构 * Department of Biomedical Informatics, Emory University（生物医学信息学系，埃默里大学） ； Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania（急诊医学系，佩尔曼医学院，宾夕法尼亚大学） 纠错

专题命中 领域大模型 ：large language model(abstract)；language model(abstract)；分类 cs.CL、cs.AI

Comments Dataset and code: https://github.com/SumonKantiDey/Reddit_Impacts_NER

One Joke to Rule them All? On the (Im)possibility of Generalizing Humor

Mor Turgeman, Chen Shani, Dafna Shahaf

机构 * The Hebrew University of Jerusalem（特拉维夫大学） ； Stanford University（斯坦福大学） 纠错

专题命中 领域大模型 ：large language model(abstract)；language model(abstract)；分类 cs.CL、cs.AI

Large VLM-based Stylized Sports Captioning

Sauptik Dhar, Nicholas Buoncristiani, Joe Anakata, Haoyu Zhang, Michelle Munson

机构 * Eluvio AI Labs（Eluvio AI实验室） 纠错

专题命中 领域大模型 ：LLM(abstract)；language model(abstract)；分类 cs.LG

Capabilities of GPT-5 across critical domains: Is it the next breakthrough?

Georgios P. Georgiou

专题命中 领域大模型 ：large language model(abstract)；language model(abstract)；分类 cs.CL

Model Science: getting serious about verification, explanation and control of AI systems

Przemyslaw Biecek, Wojciech Samek

机构 * Centre for Credible AI, Warsaw University of Technology, University of Warsaw（可信AI研究中心，华沙技术大学，华沙大学） ； Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Technical University of Berlin, Germany BIFOLD - Berlin Institute for the Foundations of Learning（人工智能系，弗劳恩霍夫 Heinrich Hertz 研究所，柏林技术大学，德国 BIFOLD - 柏林学习与数据基础研究所） 纠错

专题命中 领域大模型 ：foundation model(abstract)；分类 cs.AI、cs.LG

Comments 8 pages

Journal ref Frontiers in AI track at European Conference on Artificial Intelligence (ECAI) 2025

Integrating SAM Supervision for 3D Weakly Supervised Point Cloud Segmentation

Lechun You, Zhonghua Wu, Weide Liu, Xulei Yang, Jun Cheng, Wei Zhou, Bharadwaj Veeravalli, Guosheng Lin

机构 * Department of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学电子与计算机工程系） ； SenseTime Research（商汤科技研究院） ； School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院） ； Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR)（信息与通信研究 institute（I2R），科技研究局（A*STAR）） ； School of Computer Science and Informatics, Cardiff University（卡迪夫大学计算机科学与信息学学院） 纠错

专题命中 领域大模型 ：foundation model(abstract)

A Hybrid Recommendation Framework for Enhancing User Engagement in Local News

Payam Pourashraf, Bamshad Mobasher

专题命中 领域大模型 ：LLM(abstract)

TAGS: 3D Tumor-Adaptive Guidance for SAM

Sirui Li, Linkai Peng, Zheyuan Zhang, Gorkem Durak, Ulas Bagci

机构 * Southern University of Science and Technology（南方科技大学） ； Northwestern University（西北大学） 纠错

专题命中 领域大模型 ：foundation model(abstract)

Comments Accepted by ICCV-APAH