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The phenomena of in-context learning has typically been thought of as "learning from examples".
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 · 1901
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A unified local and global model for discourse coherence
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Modeling local coherence: An entity-based approach
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Beyond convexity: Submodularity in machine learning
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Lexical cohesion and corpus linguistics , volume 17
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The probabilistic relevance framework: Bm25 and beyond
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Multi-document summarization via budgeted maximization of submodular functions
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Casmacat: A computer-assisted translation workbench
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A short analysis of discourse coherence
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The multitarget ted talks task
Kevin Duh. 2018 · 2018
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MTNT: A testbed for machine translation of noisy text
Paul Michel and Graham Neubig. 2018 · 2018
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A call for clarity in reporting BLEU scores
Matt Post. 2018 · 2018
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Findings of the WMT 2019 biomedical translation shared task: Evaluation for MEDLINE abstracts and biomedical terminologies
Rachel Bawden, Kevin Bretonnel Cohen, Cristian Grozea, Antonio Jimeno Yepes, Madeleine Kittner, Martin Krallinger, Nancy Mah, Aurelie Neveol, Mariana Neves, Felipe Soares, Amy Siu, Karin Verspoor, and Maika Vicente Navarro. 2019 · 2019
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Billion-scale similarity search with GPUs
Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2019 · 2019
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An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2021 · 2019
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Few-shot learning with multilingual language models
Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, et al. 2021 · 2021
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What makes good in-context examples for gpt- 3 3 ?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2021 · 2021
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Prompt programming for large language models: Beyond the few-shot paradigm
Laria Reynolds and Kyle McDonell. 2021 · 2021
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Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2021 · 2021
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Do prompt-based models really understand the meaning of their prompts?
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Monolingual adapters for zero-shot neural machine translation
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On the opportunities and risks of foundation models
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In-context examples selection for machine translation
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Demystifying prompts in language models via perplexity estimation
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The bigscience roots corpus: A 1.6 tb composite multilingual dataset
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Rethinking the role of demonstrations: What makes in-context learning work?
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Training language models to follow instructions with human feedback
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A recipe for arbitrary text style transfer with large language models
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Bloom: A 176b-parameter open-access multilingual language model
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