Are Sample-Efficient NLP Models More Robust?

Are Sample-Efficient NLP Models More Robust?

12 October 2022

Robin Jia

Papers citing "Are Sample-Efficient NLP Models More Robust?"

9 / 9 papers shown

Title
IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions Ziheng Zeng K. Cheng Srihari Venkat Nanniyur Jianing Zhou Suma Bhat 24 3 0 11 Dec 2023
Enhancing PLM Performance on Labour Market Tasks via Instruction-based Finetuning and Prompt-tuning with Rules Jarno Vrolijk David Graus 30 2 0 31 Aug 2023
Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understanding Venkata Prabhakara Sarath Nookala Gaurav Verma Subhabrata Mukherjee Srijan Kumar ELM 60 6 0 19 Jun 2023
Extensible Prompts for Language Models on Zero-shot Language Style Customization Tao Ge Jing Hu Li Dong Shaoguang Mao Yanqiu Xia Xun Wang Si-Qing Chen Furu Wei VLM 44 6 0 01 Dec 2022
Exploring The Landscape of Distributional Robustness for Question Answering Models Anas Awadalla Mitchell Wortsman Gabriel Ilharco Sewon Min Ian H. Magnusson Hannaneh Hajishirzi Ludwig Schmidt ELM OOD KELM 72 19 0 22 Oct 2022
Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning Prasetya Ajie Utama N. Moosavi Victor Sanh Iryna Gurevych AAML 61 35 0 09 Sep 2021
The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester Rami Al-Rfou Noah Constant VPVLM 280 3,848 0 18 Apr 2021
Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao Adam Fisch Danqi Chen 241 1,919 0 31 Dec 2020
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference Timo Schick Hinrich Schütze 258 1,589 0 21 Jan 2020