One size doesn't fit all: Predicting the Number of Examples for In-Context Learning

11 March 2024

Papers citing "One size doesn't fit all: Predicting the Number of Examples for In-Context Learning"

8 / 8 papers shown

Title
Few-shot In-context Learning for Knowledge Base Question Answering Tianle Li Xueguang Ma Alex Zhuang Yu Gu Yu-Chuan Su Wenhu Chen 94 75 0 02 May 2023
Ask Me Anything: A simple strategy for prompting language models Simran Arora A. Narayan Mayee F. Chen Laurel J. Orr Neel Guha Kush S. Bhatia Ines Chami Frederic Sala Christopher Ré ReLM LRM 200 205 0 05 Oct 2022
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 303 11,730 0 04 Mar 2022
Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity Yao Lu Max Bartolo Alastair Moore Sebastian Riedel Pontus Stenetorp AILaw LRM 274 1,114 0 18 Apr 2021
What Makes Good In-Context Examples for GPT- $3$ ? Jiachang Liu Dinghan Shen Yizhe Zhang Bill Dolan Lawrence Carin Weizhu Chen AAML RALM 275 1,296 0 17 Jan 2021
The Pile: An 800GB Dataset of Diverse Text for Language Modeling Leo Gao Stella Biderman Sid Black Laurence Golding Travis Hoppe ... Horace He Anish Thite Noa Nabeshima Shawn Presser Connor Leahy AIMat 245 1,977 0 31 Dec 2020
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference Timo Schick Hinrich Schütze 258 1,584 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 294 6,927 0 20 Apr 2018