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Publicly available source-code libraries are continuously growing and changing.
Accelerating large-scale inference with anisotropic vector quantization
Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, and Sanjiv Kumar · 1908
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 1910
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Rtfm: Generalising to novel environment dynamics via reading
Victor Zhong, Tim Rocktäschel, and Edward Grefenstette · 1910
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Relevance weighting of search terms
Stephen E Robertson and K Sparck Jones · 1976
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The relevance of software documentation, tools and technologies: a survey
Andrew Forward and Timothy C Lethbridge · 2002
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Realm: Retrieval-augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang · 2002
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What programmers really want: results of a needs assessment for sdk documentation
Janet Nykaza, Rhonda Messinger, Fran Boehme, Cherie L Norman, Matthew Mace, and Manuel Gordon · 2002
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How software engineers use documentation: The state of the practice
Timothy C Lethbridge, Janice Singer, and Andrew Forward · 2003
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Learning to win by reading manuals in a Monte-Carlo framework
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How do professional developers comprehend software?
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Bimodal modelling of source code and natural language
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Data-driven program completion
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Abstract syntax networks for code generation and semantic parsing
Maxim Rabinovich, Mitchell Stern, and Dan Klein · 2017
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A syntactic neural model for general-purpose code generation
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A retrieve-and-edit framework for predicting structured outputs
Tatsunori B Hashimoto, Kelvin Guu, Yonatan Oren, and Percy S Liang · 2018
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Retrieval-based neural code generation
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Mapping language to code in programmatic context
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer · 2018
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Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, and Oleksandr Polozov · 2019
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Evaluating large language models trained on code
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Leveraging passage retrieval with generative models for open domain question answering
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Controllable semantic parsing via retrieval augmentation
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Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2019
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Billion-scale similarity search with GPUs
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Retrieval-augmented generation for knowledge-intensive NLP tasks
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Memorizing transformers
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The impact of lexical and grammatical processing on generating code from natural language
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