Are Prompt-based Models Clueless?

19 May 2022

Papers citing "Are Prompt-based Models Clueless?"

7 / 7 papers shown

Title
Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations Lifan Yuan Yangyi Chen Ganqu Cui Hongcheng Gao Fangyuan Zou Xingyi Cheng Heng Ji Zhiyuan Liu Maosong Sun 32 72 0 07 Jun 2023
Evaluating the Robustness of Discrete Prompts Yoichi Ishibashi Danushka Bollegala Katsuhito Sudoh Satoshi Nakamura 21 18 0 11 Feb 2023
State-of-the-art generalisation research in NLP: A taxonomy and review Dieuwke Hupkes Mario Giulianelli Verna Dankers Mikel Artetxe Yanai Elazar ... Leila Khalatbari Maria Ryskina Rita Frieske Ryan Cotterell Zhijing Jin 114 93 0 06 Oct 2022
Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao Adam Fisch Danqi Chen 241 1,918 0 31 Dec 2020
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference Timo Schick Hinrich Schütze 258 1,587 0 21 Jan 2020
Hypothesis Only Baselines in Natural Language Inference Adam Poliak Jason Naradowsky Aparajita Haldar Rachel Rudinger Benjamin Van Durme 190 576 0 02 May 2018
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 297 6,950 0 20 Apr 2018