语言大模型 / LLM

Learning to Inference Adaptively for Multimodal Large Language Models

Zhuoyan Xu, Khoi Duc Nguyen, Preeti Mukherjee, Saurabh Bagchi, Somali Chaterji, Yingyu Liang, Yin Li

机构 * University of Wisconsin-Madison（威斯康星大学麦迪逊分校） ； Purdue University（普渡大学） ； The University of Hong Kong（香港大学） 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.AI、cs.LG

Comments Published at ICCV 2025

Insights into resource utilization of code small language models serving with runtime engines and execution providers

Francisco Durán, Matias Martinez, Patricia Lago, Silverio Martínez-Fernández

机构 * Vrije Universiteit Amsterdam（阿姆斯特丹自由大学） 纠错

专题命中 效率与部署 ：language model(title,abstract)；small language model(title,abstract)；分类 cs.AI、cs.LG

Comments Accepted in Journal of Systems and Software (JSS). For its published version refer to the Journal of JSS

SmallThinker: A Family of Efficient Large Language Models Natively Trained for Local Deployment

Yixin Song, Zhenliang Xue, Dongliang Wei, Feiyang Chen, Jianxiang Gao, Junchen Liu, Hangyu Liang, Guangshuo Qin, Chengrong Tian, Bo Wen, Longyu Zhao, Xinrui Zheng, Zeyu Mi, Haibo Chen

机构 * Institute of Parallel and Distributed Systems, Shanghai Jiao Tong University（并行与分布式系统研究所，上海交通大学） ； School of Artificial Intelligence, Shanghai Jiao Tong University（人工智能学院，上海交通大学） ； Zenergize AI 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.AI、cs.LG

Enhancing Ultra-Low-Bit Quantization of Large Language Models Through Saliency-Aware Partial Retraining

Deyu Cao, Samin Aref

机构 * The University of Tokyo（东京大学） ； Department of Mechanical and Industrial Engineering, University of Toronto（多伦多大学机械与工业工程系） 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.LG

Comments This is a post-peer-review accepted manuscript from the proceedings of the 22nd International Conference on Modeling Decisions for Artificial Intelligence (MDAI'25). The publisher authenticated version and full citation details are available on Springer's website (LNAI 15957). https://doi.org/10.1007/978-3-032-00891-6_28

Accelerating Multimodal Large Language Models via Dynamic Visual-Token Exit and the Empirical Findings

Qiong Wu, Wenhao Lin, Yiyi Zhou, Weihao Ye, Zhanpeng Zen, Xiaoshuai Sun, Rongrong Ji

机构 * Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学） ； Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院） 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.LG

Detecting and Pruning Prominent but Detrimental Neurons in Large Language Models

Ameen Ali, Shahar Katz, Lior Wolf, Ivan Titov

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.LG

Enhancing Vaccine Safety Surveillance: Extracting Vaccine Mentions from Emergency Department Triage Notes Using Fine-Tuned Large Language Models

Sedigh Khademi, Jim Black, Christopher Palmer, Muhammad Javed, Hazel Clothier, Jim Buttery, Gerardo Luis Dimaguila

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

Comments 5 pages

Journal ref Medinfo 2025

Single-to-mix Modality Alignment with Multimodal Large Language Model for Document Image Machine Translation

Yupu Liang, Yaping Zhang, Zhiyang Zhang, Yang Zhao, Lu Xiang, Chengqing Zong, Yu Zhou

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

Comments Accepted by ACL 2025 Main

ChemMiner: A Large Language Model Agent System for Chemical Literature Data Mining

Kexin Chen, Yuyang Du, Junyou Li, Hanqun Cao, Menghao Guo, Xilin Dang, Lanqing Li, Jiezhong Qiu, Pheng Ann Heng, Guangyong Chen

机构 * The Chinese University of Hong Kong, New Territories, Hong Kong SAR（香港中文大学） ； Zhejiang Lab, Zhejiang University, Hangzhou, China（浙江大学杭州实验室） ； Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China（中国科学院杭州医学研究所） 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.AI、cs.LG

AgentStealth: Reinforcing Large Language Model for Anonymizing User-generated Text

Chenyang Shao, Tianxing Li, Chenhao Pu, Fengli Xu, Yong Li

专题命中 效率与部署 ：language model(title,abstract)；large language model(title)；LLM(abstract)；分类 cs.CL、cs.AI

Comments This work has been submitted to NeurIPS 2025. Under review

Large Language Models as symbolic DNA of cultural dynamics

Parham Pourdavood, Michael Jacob, Terrence Deacon

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

Comments 28 pages, 1 figure

EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models

Chengyu Wang, Junbing Yan, Wenrui Cai, Yuanhao Yue, Jun Huang

机构 * Alibaba Cloud Computing（阿里云计算） ； Shanghai Jiao Tong University（上海交通大学） 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling

Yuxuan Yue, Zukang Xu, Zhihang Yuan, Dawei Yang, Jianlong Wu, Liqiang Nie

机构 * Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）） ； Houmo AI 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.AI、cs.LG

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models

Kejia Chen, Jiawen Zhang, Jiacong Hu, Yu Wang, Jian Lou, Zunlei Feng, Mingli Song

机构 * The State Key Laboratory of Blockchain and Data Security, Zhejiang University（区块链与数据安全国家重点实验室，浙江大学） ； Sun Yat-sen University（中山大学） ； Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江）区块链与数据安全研究院） 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.AI、cs.LG

Comments ICML 2025

A Closer Look into Mixture-of-Experts in Large Language Models

Ka Man Lo, Zeyu Huang, Zihan Qiu, Zili Wang, Jie Fu

机构 * University of Macau（澳门大学） ； University of Edinburgh（爱丁堡大学） ； Tsinghua University（清华大学） ； INF Technology（INF技术） ； Shanghai AI Lab（上海人工智能实验室） 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.LG

Comments NAACL 2025 Findings

On Almost Surely Safe Alignment of Large Language Models at Inference-Time

Xiaotong Ji, Shyam Sundhar Ramesh, Matthieu Zimmer, Ilija Bogunovic, Jun Wang, Haitham Bou Ammar

机构 * Huawei Noah’s Ark Lab（华为诺亚实验室） ； Imperial College London（帝国理工学院伦敦分校） ； University College London（伦敦大学学院） ； UCL Centre for Artificial Intelligence（UCL人工智能中心） 纠错

专题命中 效率与部署 ：large language model(title)；language model(title)；LLM(abstract)；RLHF(abstract)

S$^4$C: Speculative Sampling with Syntactic and Semantic Coherence for Efficient Inference of Large Language Models

Tao He, Guang Huang, Yu Yang, Tianshi Xu, Sicheng Zhao, Guiguang Ding, Pengyang Wang, Feng Tian

机构 * GRG Banking Equipment Co., Ltd（GRG银行设备有限公司） ； South China University of Technology（华南理工大学） ； University of Macau（澳门大学） ； Tsinghua University（清华大学） 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models

Junho Yoon, Geom Lee, Donghyeon Jeon, Inho Kang, Seung-Hoon Na

机构 * Jeonbuk National University（全州国立大学） ； Naver ； UNIST（全南国立科学技术大学） 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

Comments 10 pages, 2 figures

Latent Multi-Head Attention for Small Language Models

Sushant Mehta, Raj Dandekar, Rajat Dandekar, Sreedath Panat

机构 * Vizuara AI Labs（Vizuara AI实验室） 纠错

专题命中 效率与部署 ：language model(title,abstract)；small language model(title,abstract)；分类 cs.CL、cs.AI

Comments 6 pages, 1 figure. 5 tables

DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference Acceleration

Hanzhi Zhang, Heng Fan, Kewei Sha, Yan Huang, Yunhe Feng

机构 * LLaVi Lab, Department of Computer Science & Engineering（LLaVi实验室，计算机科学与工程系） ； Department of Data Science（数据科学系） 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

Persistent Topological Features in Large Language Models

Yuri Gardinazzi, Karthik Viswanathan, Giada Panerai, Alessio Ansuini, Alberto Cazzaniga, Matteo Biagetti

机构 * Area Science Park, Trieste, Italy（特雷斯特科学公园） ； University of Trieste, Trieste, Italy（特雷斯特大学） ； University of Amsterdam, Amsterdam, the Netherlands（阿姆斯特丹大学） 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.LG

Comments 10+17 pages, 17 figures, 3 tables. Accepted as poster at ICML 2025

Prompt-based Depth Pruning of Large Language Models

Juyun Wee, Minjae Park, Jaeho Lee

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

Comments Project: https://jwee01.github.io/PuDDing/ Code: https://github.com/tada0347/PuDDing

Deploying Open-Source Large Language Models: A performance Analysis

Yannis Bendi-Ouis, Dan Dutartre, Xavier Hinaut

机构 * Mnemosyne Inria Bordeaux, France（Mnemosyne 国家信息与自动化技术研究院波尔多分部） ； SED Inria Bordeaux, France（SED 国家信息与自动化技术研究院波尔多分部） 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.AI、cs.LG

Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices

Sara Abdali, Richard Anarfi, CJ Barberan, Jia He, Erfan Shayegani

机构 * Microsoft（微软） ； University of California, Riverside (UCR)（加州大学河滨分校） 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.AI、cs.LG

Token Constraint Decoding Improves Robustness on Question Answering for Large Language Models

Jui-Ming Yao, Hao-Yuan Chen, Zi-Xian Tang, Bing-Jia Tan, Sheng-Wei Peng, Bing-Cheng Xie, Shun-Feng Su

机构 * Department of Computer Science and Information Engineering（计算机科学与信息工程系） ； National Taiwan University of Science and Technology（台湾科技大学） ； Bachelor’s Program in Computer Science（计算机科学学士班） ； University of London（伦敦大学） 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

Olica: Efficient Structured Pruning of Large Language Models without Retraining

Jiujun He, Huazhen Lin

机构 * Center of Statistical Research, School of Statistics（统计研究中心、统计学与数据科学学院） ； New Cornerstone Science Laboratory, Southwestern University of Finance（新基石科学实验室、西南财经大学） 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.LG

Comments Accepted to ICML 2025

High-Throughput Phenotyping of Clinical Text Using Large Language Models

Daniel B. Hier, S. Ilyas Munzir, Anne Stahlfeld, Tayo Obafemi-Ajayi, Michael D. Carrithers

机构 * Dept. of Neurology \& Rehabilitation University of Illinois at Chicago Chicago, IL USA ； Engineering Program Missouri State University Springfield, MO USA 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

Comments Submitted to IEEE-EMBS International Conference on Biomedical and Health Informatics, Houston TX

Journal ref 2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, Houston TX, USA pp. 1-8

Large Language Models Can Be a Viable Substitute for Expert Political Surveys When a Shock Disrupts Traditional Measurement Approaches

Patrick Y. Wu

机构 * Department of Computer Science（计算机科学系） ； American University（美国大学） 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

Comments 19 pages, 6 figures

Unified Game Moderation: Soft-Prompting and LLM-Assisted Label Transfer for Resource-Efficient Toxicity Detection

Zachary Yang, Domenico Tullo, Reihaneh Rabbany

机构 * Ubisoft La Forge（育碧La Forge） ； McGill University（麦吉尔大学） ； Mila ； CIFAR AI Chair（CIFAR人工智能主席） 纠错

专题命中 效率与部署 ：LLM(title,abstract)；prompting(title,abstract)；分类 cs.CL、cs.AI

Comments 11 pages, 1 figure, 9 Tables, KDD 2025 ADS Track

AhaKV: Adaptive Holistic Attention-Driven KV Cache Eviction for Efficient Inference of Large Language Models

Yifeng Gu, Zicong Jiang, Jianxiu Jin, Kailing Guo, Ziyang Zhang, Xiangmin Xu

机构 * South China University of Technology（南方科技大学） ； Pazhou Laboratory（琶洲实验室） 纠错

专题命中 效率与部署 ：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

Comments 14 pages, 8 figures