arXiv:2609.36448v1 Announce Type: new
Abstract: Transformers have demonstrated remarkable in-context learning (ICL) capabilities, enabling them to perform new tasks without additional fine-tuning. Ho...
By Junze Deng, Daouda Sow, Sen Lin, Yingbin Liang
arXiv:2607. 00479v1 Announce Type: new Abstract: Transformer-based large models have demonstrated remarkable generalization abilities across different tasks by leveraging a context-aware attention module for in-context learning.
By Peilin Liu, Ding-Xuan Zhou
arXiv:2607. 03660v1 Announce Type: cross Abstract: Modern sequence models have a striking capacity for in-context learning (ICL); they can perform new tasks based only on examples given in the prompt.
By Mary Letey, Yue M. Lu, Cengiz Pehlevan, Jacob Zavatone-Veth
arXiv:2606. 06814v1 Announce Type: cross Abstract: The transformer's emergent ability to perform in-context learning (ICL) has sparked a wide range of studies designed to understand its underlying mechanisms.
By Soo Min Kwon, Alec S. Xu, Can Yaras, Dogyoon Song, Laura Balzano, Qing Qu
arXiv:2606. 05134v1 Announce Type: cross Abstract: Deep active learning has previously been explored for LLM in-context sample selection, but not with methods that utilise recent advances in understanding of transformer activations.
By Yaseen M. Osman, Geoff V. Merrett, Stuart E. Middleton
arXiv:2609.07086v1 Announce Type: new
Abstract: Transformers are a dominant architecture in modern machine learning, powering applications across vision, language, and beyond. At the core of their su...
By Hemanth Saratchandran, Simon Lucey