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Large language models (LLMs) have recently shown great potential for in-context learning, where LLMs learn a new task simply by conditioning on a few input-label pairs (prompts).
Language models are few-shot learners
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Recursive deep models for semantic compositionality over a sentiment treebank
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" why should i trust you?" explaining the predictions of any classifier
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Adversarial examples are not bugs, they are features
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
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Predicting the Type and Target of Offensive Posts in Social Media
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The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al. 2020 · 2020
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann. 2020 · 2020
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Beyond accuracy: Behavioral testing of nlp models with checklist
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020 · 2020
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An empirical study on robustness to spurious correlations using pre-trained language models
Lifu Tu, Garima Lalwani, Spandana Gella, and He He. 2020 · 2020
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Badnl: Backdoor attacks against nlp models
Xiaoyi Chen, Ahmed Salem, Michael Backes, Shiqing Ma, and Yang Zhang. 2021 · 2021
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Identifying and mitigating spurious correlations for improving robustness in nlp models
Tianlu Wang, Diyi Yang, and Xuezhi Wang. 2021 · 2021
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Wenkai Yang, Yankai Lin, Peng Li, Jie Zhou, and Xu Sun. 2021 · 2021
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Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
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Meta-learning via language model in-context tuning
Yanda Chen, Ruiqi Zhong, Sheng Zha, George Karypis, and He He. 2022 · 2022
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Prompt-augmented linear probing: Scaling beyond the limit of few-shot in-context learners
Hyunsoo Cho, Hyuhng Joon Kim, Junyeob Kim, Sang-Woo Lee, Sang-goo Lee, Kang Min Yoo, and Taeuk Kim. 2022 · 2022
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Why machine reading comprehension models learn shortcuts?
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Learning to retrieve prompts for in-context learning
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Multitask prompted training enables zero-shot task generalization
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Shortcut learning of large language models in natural language understanding: A survey
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Few-shot (dis) agreement identification in online discussions with regularized and augmented meta-learning
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Chain of thought prompting elicits reasoning in large language models
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
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Adversarial examples for evaluating reading comprehension systems
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