Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning
The Chinese University of Hong Kong（香港中文大学）；Tongji University（同济大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
In this study, we present aLLM4TS, an innovative framework that adapts Large Language Models (LLMs) for time-series representation learning. Central to our approach is that we reconceive time-series forecasting as a self-supervised, multi-patch prediction task, which, compared to traditional contrastive learning or mask-and-reconstruction methods, captures temporal dynamics in patch representations more effectively. Our strategy encompasses two-stage training: (i). a causal continual pre-training phase on various time-series datasets, anchored on next patch prediction, effectively syncing LLM capabilities with the intricacies of time-series data; (ii). fine-tuning for multi-patch prediction in the targeted time-series context. A distinctive element of our framework is the patch-wise decoding layer, which departs from previous methods reliant on sequence-level decoding. Such a design directly transposes individual patches into temporal sequences, thereby significantly bolstering the model's proficiency in mastering temporal patch-based representations. aLLM4TS demonstrates superior performance in several downstream tasks, proving its effectiveness in deriving temporal representations with enhanced transferability and marking a pivotal advancement in the adaptation of LLMs for time-series analysis.
ANNEAL: Adapting LLM Agents via Governed Symbolic Patch Learning
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；University at Buffalo（布法罗大学）；University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Colorado Colorado Springs（科罗拉多大学科罗拉多州立分校）
Causal-aware Large Language Models: Enhancing Decision-Making Through Learning, Adapting and Acting
School of Computer Science, Guangdong University of Technology（广东技术大学计算机科学学院）；College of Mathematics and Computer Science, Shantou University（汕头大学数学与计算机科学学院）；Huawei Noah’s Ark Lab, Huawei（华为诺亚实验室）；Peng Cheng Laboratory, Shenzhen, China（鹏城实验室）
Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and Management
Qing Yuan Research Institute, SEIEE, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院清源研究院）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；Qingpu Branch of Zhongshan Hospital, Affiliated to Fudan University（复旦大学附属中山医院青浦分院）
Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models
Department of Computer Science and Engineering, The Ohio State University（计算机科学与工程系，俄亥俄州立大学）；Translational Data Analytics Institute, The Ohio State University（转化数据分析研究所，俄亥俄州立大学）；Department of Biomedical Informatics, The Ohio State University（生物医学信息学系，俄亥俄州立大学）
TransformLLM: Adapting Large Language Models via LLM-Transformed Reading Comprehension Text
School of Mathematical Sciences（数学科学学院）；Tel Aviv University（特拉维夫大学）；Department of Electrical Engineering（电气工程系）；The Faculty of Engineering（工程学院）；Bar Ilan University（巴伊兰大学）
REFN: A Reinforcement-Learning-From-Network Framework against 1-day/n-day Exploitations
School of Artificial Intelligence, Hubei University（湖北大学人工智能学院）；Huazhong University of Science and Technology（华中科技大学）；University of Southern California（美国南加州大学）
Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift
College of Intelligence and Computing, Tianjin University, China（天津大学智能计算学院）；I2R, Agency for Science, Technology and Research, Singapore（新加坡科技研究局I2R）；Department of Computer Science, Khalifa University, UAE（阿联酋卡利法大学计算机科学系）
Towards Adapting Open-Source Large Language Models for Expert-Level Clinical Note Generation
Mayo Clinic Health System（梅奥诊所健康系统）；School of Computing and Data Science, UIUC（UIUC计算与数据科学学院）；Mayo Clinic Rochester（梅奥诊所罗切斯特分部）；Carle Illinois College of Medicine, UIUC（UIUC卡勒伊利诺伊医学院）
