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Large language models (LLMs) have already revolutionized code generation, after being pretrained on publicly available code data.
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Language models are few-shot learners
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Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, and Guillaume Lample · 2020
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Incorporating external knowledge through pre-training for natural language to code generation
Frank F. Xu, Zhengbao Jiang, Pengcheng Yin, Bogdan Vasilescu, and Graham Neubig · 2020
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Graph-based, self-supervised program repair from diagnostic feedback
Michihiro Yasunaga and Percy Liang · 2020
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Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou · 2022
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Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola · 2022
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Sparks of artificial general intelligence: Early experiments with gpt-4, 2023
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CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
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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
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A review on language models as knowledge bases
Badr AlKhamissi, Millicent Li, Asli Celikyilmaz, Mona Diab, and Marjan Ghazvininejad · 2022
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Improving language models by retrieving from trillions of tokens
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Teaching large language models to self-debug
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Augmented language models: a survey
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Codegen: An open large language model for code with multi-turn program synthesis
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Docprompting: Generating code by retrieving the docs
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