With in-context learning ability, the performance of large language models can be significantly boosted when provided with appropriate context. However, existing in-context learning methods mainly rely on human-provided contexts, such as labeled examples and explicit instructions. Writing context by humans is labor-intensive on various tasks and limits the model to tasks manageable by humans. To overcome these limitations, we propose Automatic In-Context Learning framework that enables the model to autonomously generate examples and instructions for problem-solving. With experiments across various models and datasets, results show that model-generated contexts outperform human-annotated contexts, including Few-Shot and Few-Shot-CoT methods, and surpass existing self-generated context methods like Zero-CoT and Auto-CoT.
Unlocking In-Context Learning for Natural Datasets Beyond Language Modelling
Computer Vision Group, University of Freiburg（弗赖堡大学计算机视觉组）；Neuromedical A.I. Lab, Medical Center -- University of Freiburg（弗赖堡大学医学神经AI实验室）；Medical Physics, Medical Center -- University of Freiburg（弗赖堡大学医学物理）；Visual Geometry Group, University of Oxford（牛津大学视觉几何组）
Beyond Many-Shot Translation: Scaling In-Context Demonstrations For Low-Resource Machine Translation
Institute of Information Science, Academia Sinica（台湾“中央研究院”信息科学研究所）；National Taiwan University of Science and Technology（台湾科技大学）；Ecole Centrale de Marseille（马赛中央理工学院）；National University of Singapore（新加坡国立大学）
State Key Lab of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University（人工智能通用智能国家重点实验室，智能科学与技术学院，北京大学）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；School of Economics, Peking University（经济学院，北京大学）；Institute for Artificial Intelligence, Peking University（人工智能研究院，北京大学）
Beyond Examples: High-level Automated Reasoning Paradigm in In-Context Learning via MCTS
Department of Automation, Tsinghua University（自动化系，清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Institution for Interdisciplinary Information Sciences, Tsinghua University（交叉信息 sciences 院，清华大学）
ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes
Department of Electrical and Computer Engineering, McGill University, Canada（麦吉尔大学电气与计算机工程系，加拿大）；Mila - Quebec Artificial Intelligence Institute, Montreal, Canada（魁北克人工智能研究所，蒙特利尔，加拿大）
In-context Demonstration Matters: On Prompt Optimization for Pseudo-Supervision Refinement
Center for Advanced Intelligence Project, RIKEN（RIKEN先进智能项目中心）；Northeastern University（东北大学）；UNC-Chapel Hill（北卡罗来纳大学教堂山分校）；Center for Advanced Intelligence Project, RIKEN Graduate School of Frontier Sciences, The University of Tokyo（RIKEN前沿科学研究生院、东京大学先进智能项目中心）
Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning
School of Data Science and Engineering, East China Normal University（华东师范大学数据科学与工程学院）；Alibaba Group（阿里巴巴集团）；School of Statistics, East China Normal University（华东师范大学统计学院）
ContextNav: Towards Agentic Multimodal In-Context Learning
The University of Queensland（昆士兰大学）；Nanjing University（南京大学）；University of California, Los Angeles（加州大学洛杉矶分校）；University of California, Merced（加州大学默塞德分校）
Benchmarking and Analyzing In-context Learning, Fine-tuning and Supervised Learning for Biomedical Knowledge Curation: a focused study on chemical entities of biological interest
Institute of Health Informatics（健康信息学研究所）；University College London（伦敦大学学院）
Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning
University of Chinese Academy of Sciences（中国科学院大学）；University of South Florida（佛罗里达州立大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Key Laboratory of System Software (Chinese Academy of Sciences)（中国科学院系统软件重点实验室）；State Key Laboratory of Computer Science (Institute of Software, Chinese Academy of Sciences)（中国科学院软件研究所计算机科学国家重点实验室）
Improving Input-label Mapping with Demonstration Replay for In-context Learning
Meituan（美团）；Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Meta AI；National Key Laboratory of General Artificial Intelligence（通用人工智能全国重点实验室）；Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）
Beyond In-Context Learning: Aligning Long-form Generation of Large Language Models via Task-Inherent Attribute Guidelines
National University of Singapore（国立新加坡大学）；Nanyang Technological University（南洋理工大学）；Institute for Infocomm Research (I 2 R)（信息通信研究院）；Salesforce AI Research（Salesforce人工智能研究）
In-context Learning Generalizes, But Not Always Robustly: The Case of Syntax
Northeastern University（东北大学）；Technion -- Israel Institute of Technology（以色列理工学院）；Google DeepMind（谷歌DeepMind）；New York University（纽约大学）；Google Research（谷歌研究院）
AutoPRM: Automating Procedural Supervision for Multi-Step Reasoning via Controllable Question Decomposition
University of Chicago（芝加哥大学）；Peking University（北京大学）；University of California, Berkeley（加州大学伯克利分校）；Carnegie Mellon University（卡内基梅隆大学）；UNC-Chapel Hill（北卡罗来纳大学教堂山分校）
