Papers
680 papers found
From Unstructured Data to In-Context Learning: Exploring What Tasks Can Be Learned and When
Kevin Christian Wibisono, Yixin Wang
On the Noise Robustness of In-Context Learning for Text Generation
Hongfu Gao, Feipeng Zhang, Wenyu Jiang et al.
Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning
Brandon Huang, Chancharik Mitra, Assaf Arbelle et al.
A Simple Image Segmentation Framework via In-Context Examples
Yang Liu, Chenchen Jing, Hengtao Li et al.
Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical Perspective
Xinhao Yao, Xiaolin Hu, Shenzhi Yang et al.
Linear Transformers are Versatile In-Context Learners
Max Vladymyrov, Johannes von Oswald, Mark Sandler et al.
ARC: A Generalist Graph Anomaly Detector with In-Context Learning
Yixin Liu, Shiyuan Li, Yu Zheng et al.
Opponent Modeling with In-context Search
Yuheng Jing, Bingyun Liu, Kai Li et al.
The Closeness of In-Context Learning and Weight Shifting for Softmax Regression
Shuai Li, Zhao Song, Yu Xia et al.
Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning
Dake Bu, Wei Huang, Andi Han et al.
Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction
Liang Wang, Qiang Liu, Shaozhen Liu et al.
Universal In-Context Approximation By Prompting Fully Recurrent Models
Aleksandar Petrov, Tom A. Lamb, Alasdair Paren et al.
Towards Global Optimal Visual In-Context Learning Prompt Selection
Chengming Xu, Chen Liu, Yikai Wang et al.
Many-Shot In-Context Learning
Rishabh Agarwal, Avi Singh, Lei Zhang et al.
Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context
Kazusato Oko, Yujin Song, Taiji Suzuki et al.
Mixture of Demonstrations for In-Context Learning
Song Wang, Zihan Chen, Chengshuai Shi et al.
A Theoretical Understanding of Self-Correction through In-context Alignment
Yifei Wang, Yuyang Wu, Zeming Wei et al.
In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness
Liam Collins, Advait Parulekar, Aryan Mokhtari et al.
Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression
Deqing Fu, Tian-Qi Chen, Robin Jia et al.
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data
Kai Helli, David Schnurr, Noah Hollmann et al.
Lever LM: Configuring In-Context Sequence to Lever Large Vision Language Models
Xu Yang, Yingzhe Peng, Haoxuan Ma et al.
Transformers are Minimax Optimal Nonparametric In-Context Learners
Juno Kim, Tai Nakamaki, Taiji Suzuki
Retrieval & Fine-Tuning for In-Context Tabular Models
Valentin Thomas, Junwei Ma, Rasa Hosseinzadeh et al.
Can large language models explore in-context?
Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster et al.
Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models
Chengshuai Shi, Kun Yang, Jing Yang et al.