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Pre-trained large language models (LLMs) have significantly improved code generation.
Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N · 2005
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An innovative approach for testing bioinformatics programs using metamorphic testing
Chen, T. Y., Ho, J. W., Liu, H · 2009
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Efficient estimation of word representations in vector space
Mikolov, T., Chen, K., Corrado, G · 2013
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A large-scale analysis of bioinformatics code on github
Russell, P. H., Johnson, R. L., Ananthan, S · 2018
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R · 2019
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A general language assistant as a laboratory for alignment
Askell, A., Bai, Y., Chen, A · 2021
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Program synthesis with large language models
Austin, J., Odena, A., Nye, M · 2021
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Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H · 2021
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Measuring coding challenge competence with apps
Hendrycks, D., Basart, S., Kadavath, S · 2021
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Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Wang, Y., Wang, W., Joty, S · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Bai, Y., Jones, A., Ndousse, K · 2022
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On the opportunities and risks of foundation models
Bommasani, R., Hudson, D. A., Adeli, E · 2022
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Codet: Code generation with generated tests
Chen, B., Zhang, F., Nguyen, A · 2022
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Palm: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J · 2022
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PanGu-Coder: program synthesis with function-level language modeling
Christopoulou, F., Lampouras, G., Gritta, M · 2022
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Eleutherai/lm-evaluation-harness: v0.3.0
Gao, L., Tow, J., Biderman, S · 2022
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Training compute-optimal large language models
Hoffmann, J., Borgeaud, S., Mensch, A · 2022
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A hazard analysis framework for code synthesis large language models
Khlaaf, H., Mishkin, P., Achiam, J · 2022
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DS-1000: a natural and reliable benchmark for data science code generation
Lai, Y., Li, C., Wang, Y · 2022
Cited alongside, same era.
Competition-level code generation with alphacode
Li, Y., Choi, D., Chung, J · 2022
Cited alongside, same era.
huggingface/tokenizers: Rust 0.13.2
MOI, A., Patry, N., Cistac, P · 2022
Cited alongside, same era.
Lamda: Language models for dialog applications
Thoppilan, R., Freitas, D. D., Hall, J · 2022
Cited alongside, same era.
GLM-130B: an open bilingual pre-trained model
Zeng, A., Liu, X., Du, Z · 2022
Cited alongside, same era.
Measuring data
Mitchell, M., Luccioni, A. S., Lambert, N · 2023
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Demystifying gpt self-repair for code generation
Olausson, T. X., Inala, J. P., Wang, C · 2023
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Gpt-4 technical report
OpenAI (2023) · 2023
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Measuring the impact of programming language distribution
Orlanski, G., Xiao, K., Garcia, X · 2023
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Understanding the effectiveness of large language models in code translation
Pan, R., Ibrahimzada, A. R., Krishna, R · 2023
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Allal, L. B., Li, R., Kocetkov, D · 2023
Cited alongside, same era.
Multi-lingual evaluation of code generation models
Athiwaratkun, B., Gouda, S. K., Wang, Z · 2023
Cited alongside, same era.
Grounded copilot: How programmers interact with code-generating models
Barke, S., James, M. B. and Polikarpova, N. (2023) · 2023
Cited alongside, same era.
Pythia: A suite for analyzing large language models across training and scaling
Biderman, S., Schoelkopf, H., Anthony, Q · 2023
Cited alongside, same era.
Codetf: One-stop transformer library for state-of-the-art code llm
Bui, N. D., Le, H., Wang, Y · 2023
Cited alongside, same era.
MultiPL-E: a scalable and polyglot approach to benchmarking neural code generation
Cassano, F., Gouwar, J., Nguyen, D · 2023
Cited alongside, same era.
Teaching large language models to self-debug
Chen, X., Lin, M., Schärli, N · 2023
Cited alongside, same era.
Patil, S. G., Zhang, T., Wang, X · 2023
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Toolllm: Facilitating large language models to master 16000+ real-world apis
Qin, Y., Liang, S., Ye, Y · 2023
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Pangu-coder2: Boosting large language models for code with ranking feedback
Shen, B., Zhang, J., Chen, T · 2023
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Repofusion: Training code models to understand your repository
Shrivastava, D., Kocetkov, D., de Vries, H · 2023
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LLaMA: open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G · 2023
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Natural language generation and understanding of big code for ai-assisted programming: A review
Wong, M.-F., Guo, S., Hang, C.-N · 2023
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On the tool manipulation capability of open-source large language models
Xu, Q., Hong, F., Li, B · 2023
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Codereval: A benchmark of pragmatic code generation with generative pre-trained models
Yu, H., Shen, B., Ran, D · 2023
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Evaluating instruction-tuned large language models on code comprehension and generation
Yuan, Z., Liu, J., Zi, Q · 2023
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Large language models meet nl2code: A survey
Zan, D., Chen, B., Zhang, F · 2023
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Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x
Zheng, Q., Xia, X., Zou, X · 2023
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Large language models are state-of-the-art evaluators of code generation
Zhuo, T. Y. (2023) · 2023
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