Disentangling Latent Shifts of In-Context Learning Through Self-Training

2 October 2024

Papers citing "Disentangling Latent Shifts of In-Context Learning Through Self-Training"

10 / 10 papers shown

Title
In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering Sheng Liu Haotian Ye Lei Xing James Y. Zou 79 110 0 11 Nov 2023
Function Vectors in Large Language Models Eric Todd Millicent Li Arnab Sen Sharma Aaron Mueller Byron C. Wallace David Bau 48 114 0 23 Oct 2023
Editing Models with Task Arithmetic Gabriel Ilharco Marco Tulio Ribeiro Mitchell Wortsman Suchin Gururangan Ludwig Schmidt Hannaneh Hajishirzi Ali Farhadi KELM MoMe MU 182 493 0 08 Dec 2022
Large Language Models Can Self-Improve Jiaxin Huang S. Gu Le Hou Yuexin Wu Xuezhi Wang Hongkun Yu Jiawei Han ReLM AI4MH LRM 171 608 0 20 Oct 2022
Self-Training: A Survey Massih-Reza Amini Vasilii Feofanov Loïc Pauletto Lies Hadjadj Emilie Devijver Yury Maximov SSL 91 103 0 24 Feb 2022
The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention Kazuki Irie Róbert Csordás Jürgen Schmidhuber 57 44 0 11 Feb 2022
Language Models are Few-Shot Learners Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan ... Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever Dario Amodei BDL 743 41,932 0 28 May 2020
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference Timo Schick Hinrich Schütze 344 1,613 0 21 Jan 2020
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 1.1K 7,154 0 20 Apr 2018
Neural Network with Unbounded Activation Functions is Universal Approximator Sho Sonoda Noboru Murata 63 335 0 14 May 2015