Selective Annotation Makes Language Models Better Few-Shot Learners

Selective Annotation Makes Language Models Better Few-Shot Learners

5 September 2022

Weijia Shi

Mari Ostendorf

Luke Zettlemoyer

Papers citing "Selective Annotation Makes Language Models Better Few-Shot Learners"

8 / 58 papers shown

Title
Binding Language Models in Symbolic Languages Zhoujun Cheng Tianbao Xie Peng Shi Chengzu Li Rahul Nadkarni ... Dragomir R. Radev Mari Ostendorf Luke Zettlemoyer Noah A. Smith Tao Yu LMTD 134 199 0 06 Oct 2022
Complexity-Based Prompting for Multi-Step Reasoning Yao Fu Hao-Chun Peng Ashish Sabharwal Peter Clark Tushar Khot ReLM LRM 164 415 0 03 Oct 2022
Multitask Prompted Training Enables Zero-Shot Task Generalization Victor Sanh Albert Webson Colin Raffel Stephen H. Bach Lintang Sutawika ... T. Bers Stella Biderman Leo Gao Thomas Wolf Alexander M. Rush LRM 218 1,663 0 15 Oct 2021
Meta-learning via Language Model In-context Tuning Yanda Chen Ruiqi Zhong Sheng Zha George Karypis He He 236 158 0 15 Oct 2021
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 281 1,124 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 1,313 0 17 Jan 2021
Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao Adam Fisch Danqi Chen 243 1,927 0 31 Dec 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 304 6,996 0 20 Apr 2018