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Current state-of-the-art large language models are effective in generating high-quality text and encapsulating a broad spectrum of world knowledge.
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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020a · 1901
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REALM: retrieval-augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020 · 2002
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The Probabilistic Relevance Framework: BM25 and Beyond
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The promises and perils of mining github
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Adam: A method for stochastic optimization
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chrF: character n-gram F-score for automatic MT evaluation
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Retrieval-based neural code generation
Shirley Anugrah Hayati, Raphael Olivier, Pravalika Avvaru, Pengcheng Yin, Anthony Tomasic, and Graham Neubig. 2018 · 2018
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NL2Bash: A corpus and semantic parser for natural language interface to the linux operating system
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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HuggingFace’s Transformers: State-of-the-art Natural Language Processing
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Neurips 2020 NLC2CMD competition: Translating natural language to bash commands
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Dense Passage Retrieval for Open-Domain Question Answering
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Generation-Augmented Retrieval for Open-domain Question Answering
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RAT-SQL: Relation-aware schema encoding and linking for text-to-SQL parsers
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Retgen: A joint framework for retrieval and grounded text generation modeling
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Guiding Language Models of Code with Global Context using Monitors
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Cocomic: Code completion by jointly modeling in-file and cross-file context
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Repository-Level Prompt Generation for Large Language Models of Code
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