2025 Poster "in-context learning" Papers
57 papers found • Page 1 of 2
Adaptive Transformer Programs: Bridging the Gap Between Performance and Interpretability in Transformers
Quoc-Vinh Lai-Dang, Taemin Kang, Seungah Son
A Recipe for Generating 3D Worlds from a Single Image
Katja Schwarz, Denis Rozumny, Samuel Rota Bulò et al.
Attention-based clustering
Rodrigo Maulen Soto, Pierre Marion, Claire Boyer
BenTo: Benchmark Reduction with In-Context Transferability
Hongyu Zhao, Ming Li, Lichao Sun et al.
Can In-context Learning Really Generalize to Out-of-distribution Tasks?
Qixun Wang, Yifei Wang, Xianghua Ying et al.
Can LLMs Reason Over Non-Text Modalities in a Training-Free Manner? A Case Study with In-Context Representation Learning
Tianle Zhang, Wanlong Fang, Jonathan Woo et al.
Competition Dynamics Shape Algorithmic Phases of In-Context Learning
Core Francisco Park, Ekdeep Singh Lubana, Hidenori Tanaka
Cropper: Vision-Language Model for Image Cropping through In-Context Learning
Seung Hyun Lee, Jijun jiang, Yiran Xu et al.
DataMan: Data Manager for Pre-training Large Language Models
Ru Peng, Kexin Yang, Yawen Zeng et al.
Density estimation with LLMs: a geometric investigation of in-context learning trajectories
Toni Liu, Nicolas Boulle, Raphaël Sarfati et al.
Differential Transformer
Tianzhu Ye, Li Dong, Yuqing Xia et al.
Efficient Cross-Episode Meta-RL
Gresa Shala, André Biedenkapp, Pierre Krack et al.
ELICIT: LLM Augmentation Via External In-context Capability
Futing Wang, Jianhao (Elliott) Yan, Yue Zhang et al.
Endless Jailbreaks with Bijection Learning
Brian R.Y. Huang, Max Li, Leonard Tang
Explore In-Context Message Passing Operator for Graph Neural Networks in A Mean Field Game
Tingting Dan, Xinwei Huang, Won Hwa Kim et al.
Exploring the Limits of Vision-Language-Action Manipulation in Cross-task Generalization
Jiaming Zhou, Ke Ye, Jiayi Liu et al.
Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass
Tong Chen, Hao Fang, Patrick Xia et al.
GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning
Haonan Yuan, Qingyun Sun, Junhua Shi et al.
How Data Mixing Shapes In-Context Learning: Asymptotic Equivalence for Transformers with MLPs
Samet Demir, Zafer Dogan
Implicit In-context Learning
Zhuowei Li, Zihao Xu, Ligong Han et al.
Improving Large Language Model Planning with Action Sequence Similarity
Xinran Zhao, Hanie Sedghi, Bernd Bohnet et al.
In-Context Learning Strategies Emerge Rationally
Daniel Wurgaft, Ekdeep S Lubana, Core Francisco Park et al.
Inference Scaling for Long-Context Retrieval Augmented Generation
Zhenrui Yue, Honglei Zhuang, Aijun Bai et al.
InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales
Zhepei Wei, Wei-Lin Chen, Yu Meng
Investigating the Pre-Training Dynamics of In-Context Learning: Task Recognition vs. Task Learning
Xiaolei Wang, Xinyu Tang, Junyi Li et al.
Is In-Context Learning Sufficient for Instruction Following in LLMs?
Hao Zhao, Maksym Andriushchenko, francesco croce et al.
Knowledge Starts with Practice: Knowledge-Aware Exercise Generative Recommendation with Adaptive Multi-Agent Cooperation
Yangtao Zhou, Hua Chu, chen et al.
Large (Vision) Language Models are Unsupervised In-Context Learners
Artyom Gadetsky, Andrei Atanov, Yulun Jiang et al.
Learning to Rank for In-Context Example Retrieval
Yuwen Ji, Luodan Zhang, Ambyer han et al.
Linear Transformers Implicitly Discover Unified Numerical Algorithms
Patrick Lutz, Aditya Gangrade, Hadi Daneshmand et al.
Nested Learning: The Illusion of Deep Learning Architectures
Ali Behrouz, Meisam Razaviyayn, Peilin Zhong et al.
Neuroverse3D: Developing In-Context Learning Universal Model for Neuroimaging in 3D
Jiesi Hu, Hanyang Peng, Yanwu Yang et al.
On Linear Representations and Pretraining Data Frequency in Language Models
Jack Merullo, Noah Smith, Sarah Wiegreffe et al.
On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery
Renpu Liu, Ruida Zhou, Cong Shen et al.
On the Robustness of Transformers against Context Hijacking for Linear Classification
Tianle Li, Chenyang Zhang, Xingwu Chen et al.
Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning
Baiyuan Chen, Shinji Ito, Masaaki Imaizumi
Optimality and NP-Hardness of Transformers in Learning Markovian Dynamical Functions
Yanna Ding, Songtao Lu, Yingdong Lu et al.
PersonalLLM: Tailoring LLMs to Individual Preferences
Thomas Zollo, Andrew Siah, Naimeng Ye et al.
Reasoning Models Better Express Their Confidence
Dongkeun Yoon, Seungone Kim, Sohee Yang et al.
REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments
Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman et al.
RelationAdapter: Learning and Transferring Visual Relation with Diffusion Transformers
Yan Gong, Yiren Song, Yicheng Li et al.
Selective induction Heads: How Transformers Select Causal Structures in Context
Francesco D'Angelo, francesco croce, Nicolas Flammarion
Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks
Vishnu Sarukkai, Zhiqiang Xie, Kayvon Fatahalian
Short-length Adversarial Training Helps LLMs Defend Long-length Jailbreak Attacks: Theoretical and Empirical Evidence
Shaopeng Fu, Liang Ding, Jingfeng ZHANG et al.
Show and Segment: Universal Medical Image Segmentation via In-Context Learning
Yunhe Gao, Di Liu, Zhuowei Li et al.
Task Descriptors Help Transformers Learn Linear Models In-Context
Ruomin Huang, Rong Ge
Technical Debt in In-Context Learning: Diminishing Efficiency in Long Context
Taejong Joo, Diego Klabjan
Theoretical Insights into In-context Learning with Unlabeled Data
Yingcong Li, Xiangyu Chang, Muti Kara et al.
TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning
Andreas Auer, Patrick Podest, Daniel Klotz et al.
Transformers are almost optimal metalearners for linear classification
Roey Magen, Gal Vardi