In-Context Learning
Learning from examples in the prompt without fine-tuning
Top Papers
Generative Multimodal Models are In-Context Learners
Quan Sun, Yufeng Cui, Xiaosong Zhang et al.
Understanding Catastrophic Forgetting in Language Models via Implicit Inference
Suhas Kotha, Jacob Springer, Aditi Raghunathan
Consistency-guided Prompt Learning for Vision-Language Models
Shuvendu Roy, Ali Etemad
Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
Han Zhou, Xingchen Wan, Lev Proleev et al.
In-Context Learning Learns Label Relationships but Is Not Conventional Learning
Jannik Kossen, Yarin Gal, Tom Rainforth
Visual In-Context Prompting
Feng Li, Qing Jiang, Hao Zhang et al.
Two-stage LLM Fine-tuning with Less Specialization and More Generalization
Yihan Wang, Si Si, Daliang Li et al.
When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations
Aleksandar Petrov, Philip Torr, Adel Bibi
Which Attention Heads Matter for In-Context Learning?
Kayo Yin, Jacob Steinhardt
Understanding In-Context Learning from Repetitions
Jianhao (Elliott) Yan, Jin Xu, Chiyu Song et al.
Semantic Residual Prompts for Continual Learning
Martin Menabue, Emanuele Frascaroli, Matteo Boschini et al.
Customizing Language Model Responses with Contrastive In-Context Learning
Xiang Gao, Kamalika Das
Context Diffusion: In-Context Aware Image Generation
Ivona Najdenkoska, Animesh Sinha, Abhimanyu Dubey et al.
Learning to Learn Better Visual Prompts
Fengxiang Wang, Wanrong Huang, Shaowu Yang et al.
Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective
Minh Le, Tien Ngoc Luu, An Nguyen The et al.
Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models
Can Demircan, Tankred Saanum, Akshay Jagadish et al.
Dual Process Learning: Controlling Use of In-Context vs. In-Weights Strategies with Weight Forgetting
Suraj Anand, Michael Lepori, Jack Merullo et al.
Context-Parametric Inversion: Why Instruction Finetuning May Not Actually Improve Context Reliance
Sachin Goyal, Christina Baek, Zico Kolter et al.
Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior
Youngjae Cho, HeeSun Bae, Seungjae Shin et al.
Implicit In-context Learning
Zhuowei Li, Zihao Xu, Ligong Han et al.
Mimic In-Context Learning for Multimodal Tasks
Yuchu Jiang, Jiale Fu, chenduo hao et al.
In-Context Learning and Occam's Razor
Eric Elmoznino, Tom Marty, Tejas Kasetty et al.
Understanding Prompt Tuning and In-Context Learning via Meta-Learning
Tim Genewein, Kevin Li, Jordi Grau-Moya et al.
The Atlas of In-Context Learning: How Attention Heads Shape In-Context Retrieval Augmentation
Patrick Kahardipraja, Reduan Achtibat, Thomas Wiegand et al.
ARTICLE: Annotator Reliability Through In-Context Learning
Sujan Dutta, Deepak Pandita, Tharindu Cyril Weerasooriya et al.
Do different prompting methods yield a common task representation in language models?
Guy Davidson, Todd Gureckis, Brenden Lake et al.
On the Loss of Context Awareness in General Instruction Fine-tuning
Yihan Wang, Andrew Bai, Nanyun Peng et al.
SEC-Prompt:SEmantic Complementary Prompting for Few-Shot Class-Incremental Learning
Ye Liu, Meng Yang
Embracing Collaboration Over Competition: Condensing Multiple Prompts for Visual In-Context Learning
Jinpeng Wang, Tianci Luo, Yaohua Zha et al.
Teaching VLMs to Localize Specific Objects from In-context Examples
Sivan Doveh, Nimrod Shabtay, Eli Schwartz et al.
Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Ruhan Wang, Zhiyong Wang, Chengkai Huang et al.
Exploring Task-Level Optimal Prompts for Visual In-Context Learning
Yan Zhu, Huan Ma, Changqing Zhang
Provoking Multi-modal Few-Shot LVLM via Exploration-Exploitation In-Context Learning
Cheng Chen, Yunpeng Zhai, Yifan Zhao et al.
All You Need is One: Capsule Prompt Tuning with a Single Vector
Yiyang Liu, James Liang, Heng Fan et al.
Evolving Prompts In-Context: An Open-ended, Self-replicating Perspective
Jianyu Wang, Zhiqiang Hu, Lidong Bing
An Image is Worth Multiple Words: Discovering Object Level Concepts using Multi-Concept Prompt Learning
Chen Jin, Ryutaro Tanno, Amrutha Saseendran et al.
Convolutional Prompting meets Language Models for Continual Learning
Anurag Roy, Riddhiman Moulick, Vinay Verma et al.
Emergence of In-Context Reinforcement Learning from Noise Distillation
Ilya Zisman, Vladislav Kurenkov, Alexander Nikulin et al.
Active Prompt Learning in Vision Language Models
Jihwan Bang, Sumyeong Ahn, Jae-Gil Lee
The mechanistic basis of data dependence and abrupt learning in an in-context classification task
Gautam Reddy Nallamala
One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning
Doyoung Kim, Susik Yoon, Dongmin Park et al.
BayesPrompt: Prompting Large-Scale Pre-Trained Language Models on Few-shot Inference via Debiased Domain Abstraction
Jiangmeng Li, Fei Song, Yifan Jin et al.
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models
Shaokun Zhang, Xiaobo Xia, Zhaoqing Wang et al.
Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit Emergence
Gouki Minegishi, Hiroki Furuta, Shohei Taniguchi et al.
On the Power of Context-Enhanced Learning in LLMs
Xingyu Zhu, Abhishek Panigrahi, Sanjeev Arora
Context is Environment
Sharut Gupta, Stefanie Jegelka, David Lopez-Paz et al.
Understanding Synthetic Context Extension via Retrieval Heads
Xinyu Zhao, Fangcong Yin, Greg Durrett
Iterative Vectors: In-Context Gradient Steering without Backpropagation
Yiting Liu, Zhi-Hong Deng
Prompt Gradient Projection for Continual Learning
Jingyang Qiao, Zhizhong Zhang, Xin Tan et al.
In-Context Fine-Tuning for Time-Series Foundation Models
Matthew Faw, Rajat Sen, Yichen Zhou et al.
RCS-Prompt: Learning Prompt to Rearrange Class Space for Prompt-based Continual Learning
Longrong Yang, Hanbin Zhao, Yunlong Yu et al.
Rethinking and Improving Visual Prompt Selection for In-Context Learning Segmentation Framework
Wei Suo, Lanqing Lai, Mengyang Sun et al.
X-Prompt: Generalizable Auto-Regressive Visual Learning with In-Context Prompting
Zeyi Sun, Ziyang Chu, Pan Zhang et al.
GalLop: Learning global and local prompts for vision-language models
Marc Lafon, Elias Ramzi, ClΓ©ment Rambour et al.
One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts
Ruochen Wang, Sohyun An, Minhao Cheng et al.
Generalization to New Sequential Decision Making Tasks with In-Context Learning
Sharath Chandra Raparthy, Eric Hambro, Robert Kirk et al.
In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering
Sheng Liu, Haotian Ye, Lei Xing et al.
In-Context Principle Learning from Mistakes
Tianjun Zhang, Aman Madaan, Luyu Gao et al.
Conditional Language Learning with Context
Xiao Zhang, Miao Li, Ji Wu
In-Context Learning Agents Are Asymmetric Belief Updaters
Johannes A. Schubert, Akshay Kumar Jagadish, Marcel Binz et al.
DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection
Zhi Zhou, Ming Yang, Jiang-Xin Shi et al.
Dual Operating Modes of In-Context Learning
Ziqian Lin, Kangwook Lee
Revisiting the Power of Prompt for Visual Tuning
Yuzhu Wang, Lechao Cheng, Chaowei Fang et al.
In-Context Language Learning: Architectures and Algorithms
Ekin AkyΓΌrek, Bailin Wang, Yoon Kim et al.
AIM: Let Any Multimodal Large Language Models Embrace Efficient In-Context Learning
Jun Gao, Qian Qiao, Tianxiang Wu et al.
Teaching LLMs How to Learn with Contextual Fine-Tuning
Younwoo Choi, Muhammad Adil Asif, Ziwen Han et al.
CoPL: Contextual Prompt Learning for Vision-Language Understanding
Koustava Goswami, Srikrishna Karanam, Prateksha Udhayanan et al.
Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts
Jiawen Zhu, Guansong Pang
Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal Learning
Jian Lang, Zhangtao Cheng, Ting Zhong et al.
Unsupervised Continual Anomaly Detection with Contrastively-Learned Prompt
Jiaqi Liu, Kai Wu, Qiang Nie et al.
Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
Yubin Wang, Xinyang Jiang, De Cheng et al.
Evolving Parameterized Prompt Memory for Continual Learning
Muhammad Rifki Kurniawan, Xiang Song, Zhiheng Ma et al.
CLiC: Concept Learning in Context
Mehdi Safaee, Aryan Mikaeili, Or Patashnik et al.
Why In-Context Learning Models are Good Few-Shot Learners?
Shiguang Wu, Yaqing Wang, Quanming Yao
Vision and Language Synergy for Rehearsal Free Continual Learning
Muhammad Anwar Masum, Mahardhika Pratama, Savitha Ramasamy et al.
Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks
Vishnu Sarukkai, Zhiqiang Xie, Kayvon Fatahalian
Toward Understanding In-context vs. In-weight Learning
Bryan Chan, Xinyi Chen, Andras Gyorgy et al.
Divergence-enhanced Knowledge-guided Context Optimization for Visual-Language Prompt Tuning
Yilun Li, Miaomiao Cheng, Xu Han et al.
Is In-Context Learning Sufficient for Instruction Following in LLMs?
Hao Zhao, Maksym Andriushchenko, francesco croce et al.
In-Context Editing: Learning Knowledge from Self-Induced Distributions
Siyuan Qi, Bangcheng Yang, Kailin Jiang et al.
Chain-of-Focus Prompting: Leveraging Sequential Visual Cues to Prompt Large Autoregressive Vision Models
Jiyang Zheng, Jialiang Shen, Yu Yao et al.
Technical Debt in In-Context Learning: Diminishing Efficiency in Long Context
Taejong Joo, Diego Klabjan
Disentangling Latent Shifts of In-Context Learning with Weak Supervision
Josip JukiΔ, Jan Ε najder