Papers
680 papers found
Large (Vision) Language Models are Unsupervised In-Context Learners
Artyom Gadetsky, Andrei Atanov, Yulun Jiang et al.
Vision Language Models are In-Context Value Learners
Yecheng Jason Ma, Joey Hejna, Chuyuan Fu et al.
RNNs are not Transformers (Yet): The Key Bottleneck on In-Context Retrieval
Kaiyue Wen, Xingyu Dang, Kaifeng Lyu
Mixture of In-Context Prompters for Tabular PFNs
Derek Qiang Xu, F Olcay Cirit, Reza Asadi et al.
Trained Transformer Classifiers Generalize and Exhibit Benign Overfitting In-Context
Spencer Frei, Gal Vardi
Video In-context Learning: Autoregressive Transformers are Zero-Shot Video Imitators
Wentao Zhang, Junliang Guo, Tianyu He et al.
RetroInText: A Multimodal Large Language Model Enhanced Framework for Retrosynthetic Planning via In-Context Representation Learning
Chenglong Kang, Xiaoyi Liu, Fei Guo
ELICIT: LLM Augmentation Via External In-context Capability
Futing Wang, Jianhao Yan, Yue Zhang et al.
Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences
Niklas Schmidinger, Lisa Schneckenreiter, Philipp Seidl et al.
Can In-context Learning Really Generalize to Out-of-distribution Tasks?
Qixun Wang, Yifei Wang, Xianghua Ying et al.
Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning
Fengyu Gao, Ruida Zhou, Tianhao Wang et al.
BenTo: Benchmark Reduction with In-Context Transferability
Hongyu Zhao, Ming Li, Lichao Sun et al.
Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning
Jiuqi Wang, Ethan Blaser, Hadi Daneshmand et al.
XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning
Alexander Nikulin, Ilya Zisman, Alexey Zemtsov et al.
Towards Auto-Regressive Next-Token Prediction: In-context Learning Emerges from Generalization
Zixuan Gong, Xiaolin Hu, Huayi Tang et al.
Task Descriptors Help Transformers Learn Linear Models In-Context
Ruomin Huang, Rong Ge
Distributional Associations vs In-Context Reasoning: A Study of Feed-forward and Attention Layers
Lei Chen, Joan Bruna, Alberto Bietti
Implicit In-context Learning
Zhuowei Li, Zihao Xu, Ligong Han et al.
Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective
Andrew Jesson, Nicolas Beltran-Velez, David Blei
WeatherGFM: Learning a Weather Generalist Foundation Model via In-context Learning
Xiangyu Zhao, Zhiwang Zhou, zhangwenlong et al.
Unsupervised Meta-Learning via In-Context Learning
Anna Vettoruzzo, Lorenzo Braccaioli, Joaquin Vanschoren et al.
Metalic: Meta-Learning In-Context with Protein Language Models
Jacob Beck, Shikha Surana, Manus McAuliffe et al.
Transformers are Universal In-context Learners
Takashi Furuya, Maarten V. de Hoop, Gabriel Peyré
Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning
Jaehyeon Son, Soochan Lee, Gunhee Kim