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.
A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting
Junhao Zhuang, Yanhong Zeng, WENRAN LIU et al.
Understanding Catastrophic Forgetting in Language Models via Implicit Inference
Suhas Kotha, Jacob Springer, Aditi Raghunathan
Synapse: Trajectory-as-Exemplar Prompting with Memory for Computer Control
Longtao Zheng, Rundong Wang, Xinrun Wang et al.
Consistency-guided Prompt Learning for Vision-Language Models
Shuvendu Roy, Ali Etemad
Making Text Embedders Few-Shot Learners
Chaofan Li, Minghao Qin, Shitao Xiao et al.
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?
Jingfeng Wu, Difan Zou, Zixiang Chen et al.
In-Context Pretraining: Language Modeling Beyond Document Boundaries
Weijia Shi, Sewon Min, Maria Lomeli et al.
Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
Han Zhou, Xingchen Wan, Lev Proleev et al.
BadCLIP: Trigger-Aware Prompt Learning for Backdoor Attacks on CLIP
Jiawang Bai, Kuofeng Gao, Shaobo Min et al.
HGPrompt: Bridging Homogeneous and Heterogeneous Graphs for Few-Shot Prompt Learning
Xingtong Yu, Yuan Fang, Zemin Liu 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.
FlexPrefill: A Context-Aware Sparse Attention Mechanism for Efficient Long-Sequence Inference
Xunhao Lai, Jianqiao Lu, Yao Luo et al.
Context is Key: A Benchmark for Forecasting with Essential Textual Information
Andrew Williams, Arjun Ashok, Étienne Marcotte et al.
Two-stage LLM Fine-tuning with Less Specialization and More Generalization
Yihan Wang, Si Si, Daliang Li et al.
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models
Lin Li, Haoyan Guan, Jianing Qiu et al.
GalLop: Learning global and local prompts for vision-language models
Marc Lafon, Elias Ramzi, Clément Rambour et al.
When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations
Aleksandar Petrov, Philip Torr, Adel Bibi
Prompting Hard or Hardly Prompting: Prompt Inversion for Text-to-Image Diffusion Models
Shweta Mahajan, Tanzila Rahman, Kwang Moo Yi et al.
Which Attention Heads Matter for In-Context Learning?
Kayo Yin, Jacob Steinhardt
Prompting Language-Informed Distribution for Compositional Zero-Shot Learning
Wentao Bao, Lichang Chen, Heng Huang et al.
MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQL
Arian Askari, Christian Poelitz, Xinye Tang
Multi-Prompts Learning with Cross-Modal Alignment for Attribute-Based Person Re-identification
Yajing Zhai, Yawen Zeng, Zhiyong Huang et al.
Adversarial Prompt Tuning for Vision-Language Models
Jiaming Zhang, Xingjun Ma, Xin Wang et al.
Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction
Senqiao Yang, Jiarui Wu, Jiaming Liu et al.
Prompt Compression with Context-Aware Sentence Encoding for Fast and Improved LLM Inference
Barys Liskavets, Maxim Ushakov, Shuvendu Roy et al.
Soft Prompt Generation for Domain Generalization
Shuanghao Bai, Yuedi Zhang, Wanqi Zhou et al.
Understanding In-Context Learning from Repetitions
Jianhao (Elliott) Yan, Jin Xu, Chiyu Song et al.
ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning
Beomyoung Kim, Joonsang Yu, Sung Ju Hwang
Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning
Xinshun Wang, Zhongbin Fang, Xia Li et al.
Exploring Conditional Multi-Modal Prompts for Zero-shot HOI Detection
Ting Lei, Shaofeng Yin, Yuxin Peng et al.
Cascade Prompt Learning for Visual-Language Model Adaptation
Ge Wu, Xin Zhang, Zheng Li et al.
Semantic Residual Prompts for Continual Learning
Martin Menabue, Emanuele Frascaroli, Matteo Boschini et al.
miniCTX: Neural Theorem Proving with (Long-)Contexts
Jiewen Hu, Thomas Zhu, Sean Welleck
ICLR: In-Context Learning of Representations
Core Francisco Park, Andrew Lee, Ekdeep Singh Lubana et al.
Unknown Prompt the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization
Mainak Singha, Ankit Jha, Shirsha Bose et al.
Customizing Language Model Responses with Contrastive In-Context Learning
Xiang Gao, Kamalika Das
OVOR: OnePrompt with Virtual Outlier Regularization for Rehearsal-Free Class-Incremental Learning
Wei-Cheng Huang, Chun-Fu Chen, Hsiang Hsu
Code-Style In-Context Learning for Knowledge-Based Question Answering
Zhijie Nie, Richong Zhang, Zhongyuan Wang et al.
Controllable Context Sensitivity and the Knob Behind It
Julian Minder, Kevin Du, Niklas Stoehr et al.
One-stage Prompt-based Continual Learning
Youngeun Kim, YUHANG LI, Priyadarshini Panda
Context Diffusion: In-Context Aware Image Generation
Ivona Najdenkoska, Animesh Sinha, Abhimanyu Dubey et al.
PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via Prompts
Zewen Chen, Haina Qin, Juan Wang et al.
Hierarchical Context Pruning: Optimizing Real-World Code Completion with Repository-Level Pretrained Code LLMs
Lei Zhang, Yunshui Li, Jiaming Li et al.
Learning to Learn Better Visual Prompts
Fengxiang Wang, Wanrong Huang, Shaowu Yang et al.
Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal Learning
Jian Lang, Zhangtao Cheng, Ting Zhong et al.
Finding Visual Task Vectors
Alberto Hojel, Yutong Bai, Trevor Darrell et al.
Compound Text-Guided Prompt Tuning via Image-Adaptive Cues
Hao Tan, Jun Li, Yizhuang Zhou et al.
Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models
Can Demircan, Tankred Saanum, Akshay Jagadish et al.
Test-Time Personalization with Meta Prompt for Gaze Estimation
Huan Liu, Julia Qi, Zhenhao Li et al.
Context Steering: Controllable Personalization at Inference Time
Zhiyang He, Sashrika Pandey, Mariah Schrum et al.
Revisiting In-context Learning Inference Circuit in Large Language Models
Hakaze Cho, Mariko Kato, Yoshihiro Sakai et al.
Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective
Minh Le, Tien Ngoc Luu, An Nguyen The 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.
Understanding prompt engineering may not require rethinking generalization
Victor Akinwande, Yiding Jiang, Dylan Sam et al.
Strategy Coopetition Explains the Emergence and Transience of In-Context Learning
Aaditya Singh, Ted Moskovitz, Sara Dragutinović et al.
Context-Parametric Inversion: Why Instruction Finetuning May Not Actually Improve Context Reliance
Sachin Goyal, Christina Baek, Zico Kolter et al.
On the Training Convergence of Transformers for In-Context Classification of Gaussian Mixtures
Wei Shen, Ruida Zhou, Jing Yang et al.
Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior
Youngjae Cho, HeeSun Bae, Seungjae Shin et al.
Task-Adaptive Saliency Guidance for Exemplar-free Class Incremental Learning
Xialei Liu, Jiang-Tian Zhai, Andrew Bagdanov et al.
Do-PFN: In-Context Learning for Causal Effect Estimation
Jake Robertson, Arik Reuter, Siyuan Guo et al.
Implicit In-context Learning
Zhuowei Li, Zihao Xu, Ligong Han et al.
RAVE: Residual Vector Embedding for CLIP-Guided Backlit Image Enhancement
Tatiana Gaintseva, Martin Benning, Greg Slabaugh
Mimic In-Context Learning for Multimodal Tasks
Yuchu Jiang, Jiale Fu, chenduo hao et al.
Bottom-Up Domain Prompt Tuning for Generalized Face Anti-Spoofing
SI-QI LIU, Qirui Wang, Pong Chi Yuen
CAPrompt: Cyclic Prompt Aggregation for Pre-Trained Model Based Class Incremental Learning
Qiwei Li, Jiahuan Zhou
Componential Prompt-Knowledge Alignment for Domain Incremental Learning
Kunlun Xu, Xu Zou, Gang Hua et al.
In-context Time Series Predictor
Jiecheng Lu, Yan Sun, Shihao Yang
Utilize the Flow Before Stepping into the Same River Twice: Certainty Represented Knowledge Flow for Refusal-Aware Instruction Tuning
Runchuan Zhu, Zhipeng Ma, Jiang Wu et al.
Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models
Lei Tang, Jinghui Qin, Wenxuan Ye et al.
ELICIT: LLM Augmentation Via External In-context Capability
Futing Wang, Jianhao (Elliott) Yan, Yue Zhang et al.
IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning
Quan Zhang, Yuxin Qi, Xi Tang et al.
Noise-assisted Prompt Learning for Image Forgery Detection and Localization
Dong Li, Jiaying Zhu, Xueyang Fu et al.
In-Context Matting
He Guo, Zixuan Ye, Zhiguo Cao et al.
OpenKD: Opening Prompt Diversity for Zero- and Few-shot Keypoint Detection
Changsheng Lu, Zheyuan Liu, Piotr Koniusz
Advancing Prompt-Based Methods for Replay-Independent General Continual Learning
Zhiqi KANG, Liyuan Wang, Xingxing Zhang et al.
Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning
Fengyu Gao, Ruida Zhou, Tianhao Wang et al.
GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model
Zixiang Ai, Zichen Liu, Yuanhang Lei et al.
In-Context Learning and Occam's Razor
Eric Elmoznino, Tom Marty, Tejas Kasetty et al.
Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks
Vishnu Sarukkai, Zhiqiang Xie, Kayvon Fatahalian
Labels Need Prompts Too: Mask Matching for Natural Language Understanding Tasks
Bo Li, Wei Ye, Quansen Wang et al.
On the Loss of Context Awareness in General Instruction Fine-tuning
Yihan Wang, Andrew Bai, Nanyun Peng et al.
Divide-Solve-Combine: An Interpretable and Accurate Prompting Framework for Zero-shot Multi-Intent Detection
Libo Qin, Qiguang Chen, Jingxuan Zhou 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.
Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning
Yujian Liu, Shiyu Chang, Tommi Jaakkola et al.
In-Context Learning Strategies Emerge Rationally
Daniel Wurgaft, Ekdeep S Lubana, Core Francisco Park et al.
Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner
Aizierjiang Aiersilan
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.
Training Free Exponential Context Extension via Cascading KV Cache
Jeff Willette, Heejun Lee, Youngwan Lee et al.
Task Descriptors Help Transformers Learn Linear Models In-Context
Ruomin Huang, Rong Ge
Open-World Dynamic Prompt and Continual Visual Representation Learning
Youngeun Kim, Jun Fang, Qin Zhang et al.
Customized Generation Reimagined: Fidelity and Editability Harmonized
Jian Jin, Yang Shen, Zhenyong Fu et al.
SEC-Prompt:SEmantic Complementary Prompting for Few-Shot Class-Incremental Learning
Ye Liu, Meng Yang
Attack on Prompt: Backdoor Attack in Prompt-Based Continual Learning
Trang Nguyen, Anh Tran, Nhat Ho
SAM-REF: Introducing Image-Prompt Synergy during Interaction for Detail Enhancement in the Segment Anything Model
Chongkai Yu, Ting Liu, Li Anqi et al.
Teaching VLMs to Localize Specific Objects from In-context Examples
Sivan Doveh, Nimrod Shabtay, Eli Schwartz et al.
Embracing Collaboration Over Competition: Condensing Multiple Prompts for Visual In-Context Learning
Jinpeng Wang, Tianci Luo, Yaohua Zha et al.