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Large Language Models (LLMs) have already become quite proficient at solving simpler programming tasks like those in HumanEval or MBPP benchmarks.
Toward automatic program synthesis
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Neuro-symbolic program synthesis
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Huggingface’s transformers: State-of-the-art natural language processing
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Language models are few-shot learners
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PyMT5: multi-mode translation of natural language and python code with transformers
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CodeBERT: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou · 2020
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Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al · 2021
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Gpt-neo: Large scale autoregressive language modeling with mesh-tensorflow
Sid Black, Gao Leo, Phil Wang, Connor Leahy, and Stella Biderman · 2021
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
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Measuring coding challenge competence with apps
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
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Yue Wang, Weishi Wang, Shafiq R. Joty, and Steven C. H. Hoi · 2021
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Fault-aware neural code rankers
Jeevana Priya Inala, Chenglong Wang, Mei Yang, Andres Codas, Mark Encarnación, Shuvendu K Lahiri, Madanlal Musuvathi, and Jianfeng Gao · 2022
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Gpt-4 vs. gpt-3.5: A concise showdown
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Ds-1000: A natural and reliable benchmark for data science code generation
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Competition-level code generation with alphacode
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Training language models to follow instructions with human feedback
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Natural language to code translation with execution
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Least-to-most prompting enables complex reasoning in large language models
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