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We evaluate the reasoning abilities of large language models in multilingual settings.
A new algorithm for data compression
Philip Gage · 1994
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Multilingual denoising pre-training for neural machine translation
Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, and Luke Zettlemoyer · 2001
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SemEval-2012 task 7: Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Andrew Gordon, Zornitsa Kozareva, and Melissa Roemmele · 2012
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Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, and Veselin Stoyanov · 2018
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Word translation without parallel data
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Numeracy for language models: Evaluating and improving their ability to predict numbers
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XQA: A cross-lingual open-domain question answering dataset
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Polyglot contextual representations improve crosslingual transfer
Phoebe Mulcaire, Jungo Kasai, and Noah A. Smith · 2019
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WiC: the word-in-context dataset for evaluating context-sensitive meaning representations
Mohammad Taher Pilehvar and Jose Camacho-Collados · 2019
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How multilingual is multilingual BERT?
Telmo Pires, Eva Schlinger, and Dan Garrette · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Social IQa: Commonsense reasoning about social interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi · 2019
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Cross-lingual alignment of contextual word embeddings, with applications to zero-shot dependency parsing
Tal Schuster, Ori Ram, Regina Barzilay, and Amir Globerson · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages
Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki · 2020
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Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov · 2020
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Xtreme: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation
Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, and Melvin Johnson · 2020
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UNIFIEDQA: Crossing format boundaries with a single QA system
Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaneh Hajishirzi · 2020
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Show your work: Scratchpads for intermediate computation with language models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al · 2021
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Multi-domain multilingual question answering
Sebastian Ruder and Avirup Sil · 2021
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XTREME-R: Towards more challenging and nuanced multilingual evaluation
Sebastian Ruder, Noah Constant, Jan Botha, Aditya Siddhant, Orhan Firat, Jinlan Fu, Pengfei Liu, Junjie Hu, Dan Garrette, Graham Neubig, and Melvin Johnson · 2021
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It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze · 2021
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Bilingual lexicon induction via unsupervised bitext construction and word alignment
Haoyue Shi, Luke Zettlemoyer, and Sida I. Wang · 2021
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XCOPA: A multilingual dataset for causal commonsense reasoning
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AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
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Program synthesis with large language models
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Evaluating large language models trained on code
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen · 2021
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Language Models are Few-shot Multilingual Learners
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mT5: A massively multilingual pre-trained text-to-text transformer
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Discrete and Soft Prompting for Multilingual Models
Mengjie Zhao and Hinrich Schütze · 2021
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Do as I can, not as I say: Grounding language in robotic affordances
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