语言大模型 / LLM

Are We Aligned? A Preliminary Investigation of the Alignment of Responsible AI Values between LLMs and Human Judgment

Asma Yamani, Malak Baslyman, Moataz Ahmed

机构 * Information and Computer Science Department, KFUPM（信息与计算机科学系，KFUPM） ； IRC for finance and digital economy, KFUPM（金融与数字经济研究中心，KFUPM） ； SDAIA-KFUPM Joint Research Center for Artificial Intelligence, KFUPM（人工智能联合研究中心，KFUPM） 纠错

专题命中 评测与基准 ：large language model(abstract)；language model(abstract)；分类 cs.AI

The Human Flourishing Geographic Index: A County-Level Dataset for the United States, 2013--2023

Stefano M. Iacus, Devika Jain, Andrea Nasuto, Giuseppe Porro, Marcello Carammia, Andrea Vezzulli

机构 * Institute for Quantitative Social Science, Harvard University（哈佛大学定量社会科学研究所） ； Center for Geographic Analysis, Harvard University（哈佛大学地理分析中心） ； Geographic Data Science Lab, University of Liverpool（利物浦大学地理数据科学实验室） ； Department of Law, Economics and Culture, University of Insubria（Insubria大学法律、经济与文化系） ； Department of Political and Social Sciences, University of Catania（卡塔尼亚大学政治与社会科学系） ； Department of Economics, University of Insubria（Insubria大学经济学系） 纠错

专题命中 评测与基准 ：large language model(abstract)；language model(abstract)；分类 cs.CL

Benchmark Designers Should "Train on the Test Set" to Expose Exploitable Non-Visual Shortcuts

Ellis Brown, Jihan Yang, Shusheng Yang, Rob Fergus, Saining Xie

机构 * New York University（纽约大学） 纠错

专题命中 评测与基准 ：large language model(abstract)；language model(abstract)

Comments Project page: https://cambrian-mllm.github.io

Advancing Equitable AI: Evaluating Cultural Expressiveness in LLMs for Latin American Contexts

Brigitte A. Mora-Reyes, Jennifer A. Drewyor, Abel A. Reyes-Angulo

机构 * Department of Applied Computing, Michigan Technological University, Houghton, MI, USA（应用计算系，密歇根技术大学，霍顿，密歇根州，美国） ； Department of Psychology and Human Factors, Michigan Technological University, Houghton, MI, USA（心理学与人因学系，密歇根技术大学，霍顿，密歇根州，美国） 纠错

专题命中 评测与基准 ：language model(abstract)；分类 cs.AI

ArchPilot: A Proxy-Guided Multi-Agent Approach for Machine Learning Engineering

Zhuowen Yuan, Tao Liu, Yang Yang, Yang Wang, Feng Qi, Kaushik Rangadurai, Bo Li, Shuang Yang

机构 * Meta Ranking AI Research（Meta 排名人工智能研究） 纠错

专题命中 评测与基准 ：LLM(abstract)；分类 cs.AI

MCTED: A Machine-Learning-Ready Dataset for Digital Elevation Model Generation From Mars Imagery

Rafał Osadnik, Pablo Gómez, Eleni Bohacek, Rickbir Bahia

机构 * European Space Agency (ESA)（欧洲航天局） ； European Space Astronomy Centre (ESAC)（欧洲空间天文中心） ； UK Research and Innovation - Innovate UK（英国研究与创新-创新英国） ； UK Space Agency (UKSA)（英国空间局） 纠错

专题命中 评测与基准 ：foundation model(abstract)；分类 cs.LG

Comments 22 pages, 21 figures

Two Causally Related Needles in a Video Haystack

Miaoyu Li, Qin Chao, Boyang Li

机构 * College of Computing and Data Science（计算与数据科学学院） ； Nanyang Technological University（南洋理工大学） 纠错

专题命中 评测与基准 ：language model(abstract)；分类 cs.AI

Comments Accepted to NeurIPS 2025 D&B Track

SAFe-Copilot: Unified Shared Autonomy Framework

Phat Nguyen, Erfan Aasi, Shiva Sreeram, Guy Rosman, Andrew Silva, Sertac Karaman, Daniela Rus

机构 * MIT CSAIL（麻省理工学院计算机科学与人工智能实验室） ； TRI（丰田研究机构） ； MIT LIDS（麻省理工学院领导力与决策科学实验室） 纠错

专题命中 评测与基准 ：language model(abstract)

HELM: Hyperbolic Large Language Models via Mixture-of-Curvature Experts

Neil He, Rishabh Anand, Hiren Madhu, Ali Maatouk, Smita Krishnaswamy, Leandros Tassiulas, Menglin Yang, Rex Ying

机构 * Yale University, USA（耶鲁大学） 纠错

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

Divide, Cache, Conquer: Dichotomic Prompting for Efficient Multi-Label LLM-Based Classification

Mikołaj Langner, Jan Eliasz, Ewa Rudnicka, Jan Kocoń

机构 * Department of Artificial Intelligence, Wroclaw Tech, Poland（人工智能系，沃拉布勒技术学院，波兰） 纠错

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

Comments 9 pages, 8 figures

Memory- and Latency-Constrained Inference of Large Language Models via Adaptive Split Computing

Mingyu Sung, Vikas Palakonda, Suhwan Im, Sunghwan Moon, Il-Min Kim, Sangseok Yun, Jae-Mo Kang

机构 * Department of Artificial Intelligence, Kyungpook National University（人工智能系，庆北国立大学） ； Department of Electrical and Computer Engineering, Queen’s University（电气与计算机工程系，皇后大学） ； Department of Information and Communications Engineering, Pukyong National University（信息与通信工程系，浦项国立大学） 纠错

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

Decoding Emergent Big Five Traits in Large Language Models: Temperature-Dependent Expression and Architectural Clustering

Christos-Nikolaos Zacharopoulos, Revekka Kyriakoglou

机构 * Université Paris 8 Vincennes – Saint-Denis（巴黎第八大学） 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 Accepted at IJCNLP-AACL 2025

ThunderServe: High-performance and Cost-efficient LLM Serving in Cloud Environments

Youhe Jiang, Fangcheng Fu, Xiaozhe Yao, Taiyi Wang, Bin Cui, Ana Klimovic, Eiko Yoneki

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

Comments MLSys 2025

PICNIC: Silicon Photonic Interconnected Chiplets with Computational Network and In-memory Computing for LLM Inference Acceleration

Yue Jiet Chong, Yimin Wang, Zhen Wu, Xuanyao Fong

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

Arrow: Adaptive Scheduling Mechanisms for Disaggregated LLM Inference Architecture

Yu Wu, Tongxuan Liu, Yuting Zeng, Siyu Wu, Jun Xiong, Xianzhe Dong, Hailong Yang, Ke Zhang, Jing Li

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

DartQuant: Efficient Rotational Distribution Calibration for LLM Quantization

Yuantian Shao, Yuanteng Chen, Peisong Wang, Jianlin Yu, Jing Lin, Yiwu Yao, Zhihui Wei, Jian Cheng

机构 * Nanjing University of Science and Technology（南京理工大学） ； School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院） ； Huawei Technologies Co., Ltd.（华为技术有限公司） 纠错

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

Comments NeurIPS 2025, 10 pages, 12 figures

Abductive Inference in Retrieval-Augmented Language Models: Generating and Validating Missing Premises

Shiyin Lin

机构 * Independent Researcher, Mountain View, CA94039, USA（独立研究者） 纠错

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

Comparing Computational Pathology Foundation Models using Representational Similarity Analysis

Vaibhav Mishra, William Lotter

机构 * Dana-Farber Cancer Institute（达纳-法伯癌症研究所） ； Brigham and Women’s Hospital & Harvard Medical School（布里奇沃特医院及哈佛医学院） 纠错

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

Comments Proceedings of the 5th Machine Learning for Health (ML4H) Symposium

Enabling Dynamic Sparsity in Quantized LLM Inference

Rongxiang Wang, Kangyuan Shu, Felix Xiaozhu Lin

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

ASAP: an Agentic Solution to Auto-optimize Performance of Large-Scale LLM Training

Yuran Ding, Xinwei Chen, Xiaofan Zhang, Zongwei Zhou

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

Comments This work has been accepted to Workshop on ML for Systems at NeurIPS 2025

TwIST: Rigging the Lottery in Transformers with Independent Subnetwork Training

Michael Menezes, Barbara Su, Xinze Feng, Yehya Farhat, Hamza Shili, Anastasios Kyrillidis

专题命中 效率与部署 ：LLM(abstract)；large language model(abstract)；language model(abstract)；post-training(abstract)

Trustworthy LLM-Mediated Communication: Evaluating Information Fidelity in LLM as a Communicator (LAAC) Framework in Multiple Application Domains

Mohammed Musthafa Rafi, Adarsh Krishnamurthy, Aditya Balu

机构 * Iowa State University（爱荷华州立大学） 纠错

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

Comments 10 pages, 4 figures. Submitted to IEEE DISTILL 2025 (co-located with IEEE TPS 2025)

How Different Tokenization Algorithms Impact LLMs and Transformer Models for Binary Code Analysis

Ahmed Mostafa, Raisul Arefin Nahid, Samuel Mulder

机构 * Ahmed Mostafa, 1Raisul Arefin, Samuel Mulder（未知） 纠错

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

Comments Publication Notice. This paper was published in the BAR 2025 Workshop (with NDSS 2025) and is for research and educational use. Copyright \c{opyright} 2025 Internet Society. All rights reserved. Personal/classroom reproduction is permitted with this notice and full paper citation. All other uses, including commercial, require prior written permission from the Internet Society

Journal ref https://www.ndss-symposium.org/wp-content/uploads/bar2025-final13.pdf

Critical Batch Size Revisited: A Simple Empirical Approach to Large-Batch Language Model Training

William Merrill, Shane Arora, Dirk Groeneveld, Hannaneh Hajishirzi

机构 * Allen Institute for AI（人工智能研究所） 纠错

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

Comments Neurips 2025

Compression Hacking: A Supplementary Perspective on Informatics Properties of Language Models from Geometric Distortion

Jianxiang Zang, Meiling Ning, Yongda Wei, Shihan Dou, Jiazheng Zhang, Nijia Mo, Binhong Li, Tao Gui, Qi Zhang, Xuanjing Huang

机构 * Computation and Artificial Intelligence Innovative College, Fudan University（复旦大学计算与人工智能创新学院） ； Beijing University of Posts and Telecommunications（北京邮电大学） ； Shanghai University of International Business and Economics（上海国际商务经济学院） ； Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）） 纠错

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

On Multilingual Encoder Language Model Compression for Low-Resource Languages

Daniil Gurgurov, Michal Gregor, Josef van Genabith, Simon Ostermann

机构 * Saarland University（萨尔兰州大学） ； German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心） ； Kempelen Institute of Intelligent Technologies (KInIT)（Kempelen智能技术研究所） ； Centre for European Research in Trusted AI (CERTAIN)（可信人工智能欧洲研究中心） 纠错

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

Comments Accepted to SRW AACL

Efficient Reinforcement Learning from Human Feedback via Bayesian Preference Inference

Matteo Cercola, Valeria Capretti, Simone Formentin

专题命中 效率与部署 ：LLM(abstract)；RLHF(abstract)；preference optimization(abstract)；分类 cs.AI、cs.LG

Block Rotation is All You Need for MXFP4 Quantization

Yuantian Shao, Peisong Wang, Yuanteng Chen, Chang Xu, Zhihui Wei, Jian Cheng

机构 * Nanjing University of Science and Technology（南京理工大学） ； School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院） ； Zhongguancun Academy（中关村学院） ； School of Computer Science, University of Sydney（悉尼大学计算机科学学院） 纠错

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

Comments 9 pages, 10 figures

Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models

Hung-Yueh Chiang, Chi-Chih Chang, Natalia Frumkin, Kai-Chiang Wu, Mohamed S. Abdelfattah, Diana Marculescu

专题命中 效率与部署 ：post-training(title)；分类 cs.CL、cs.AI、cs.LG

BFM-Zero: A Promptable Behavioral Foundation Model for Humanoid Control Using Unsupervised Reinforcement Learning

Yitang Li, Zhengyi Luo, Tonghe Zhang, Cunxi Dai, Anssi Kanervisto, Andrea Tirinzoni, Haoyang Weng, Kris Kitani, Mateusz Guzek, Ahmed Touati, Alessandro Lazaric, Matteo Pirotta, Guanya Shi

专题命中 效率与部署 ：foundation model(title,abstract)