Are Language Models Worse than Humans at Following Prompts? It’s Complicated

Type: Article

Publication Date: 2023-01-01

Citations: 3

DOI: https://doi.org/10.18653/v1/2023.findings-emnlp.514

Locations

  • arXiv (Cornell University) - View - PDF

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Works Cited by This (13)

Action Title Year Authors
+ Character-level Convolutional Networks for Text Classification 2015 Xiang Zhang
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Yann LeCun
+ PDF Chat XNLI: Evaluating Cross-lingual Sentence Representations 2018 Alexis Conneau
Ruty Rinott
Guillaume Lample
Adina Williams
Samuel Bowman
Holger Schwenk
Veselin Stoyanov
+ A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference 2018 Adina Williams
Nikita Nangia
Samuel Bowman
+ PDF Chat BLiMP: The Benchmark of Linguistic Minimal Pairs for English 2020 Alex Warstadt
Alicia Parrish
Haokun Liu
Anhad Mohananey
Wei Peng
Sheng‐Fu Wang
Samuel R. Bowman
+ PDF Chat Do Prompt-Based Models Really Understand the Meaning of Their Prompts? 2022 Albert Webson
Ellie Pavlick
+ Chain-of-Thought Prompting Elicits Reasoning in Large Language Models 2022 Jason Lee
Xuezhi Wang
Dale Schuurmans
Maarten Bosma
Ed H.
Quoc V. Le
Denny Zhou
+ Least-to-Most Prompting Enables Complex Reasoning in Large Language Models 2022 Denny Zhou
Nathanael Schärli
Le Hou
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Nathan Scales
Xuezhi Wang
Dale Schuurmans
Claire Cui
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+ Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models 2022 Aarohi Srivastava
Abhinav Rastogi
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Abu Awal Shoeb
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+ Language models show human-like content effects on reasoning tasks 2022 Ishita Dasgupta
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+ Finetuned Language Models Are Zero-Shot Learners 2021 Jason Lee
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Vincent Y. Zhao
Kelvin Guu
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Nan Du
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