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Large language models (LLMs), such as Codex and GPT-4, have recently showcased their remarkable code generation abilities, facilitating a significant boost in coding efficiency.
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Approximate nearest neighbor negative contrastive learning for dense text retrieval
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CodeBLEU: a method for automatic evaluation of code synthesis
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Kingma, D.P., Ba, J., 2014 · 2014
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Scheller, T., Kühn, E., 2015 · 2015
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Derr, E., Bugiel, S., Fahl, S., Acar, Y., Backes, M., 2017 · 2017
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BERT: Pre-training of deep bidirectional transformers for language understanding, in: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186
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Alrubaye, H., Mkaouer, M.W., Khokhlov, I., Reznik, L., Ouni, A., Mcgoff, J., 2020 · 2020
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PyMT5: Multi-mode translation of natural language and python code with transformers, in: Conference on Empirical Methods in Natural Language Processing, pp. 9052–9065
Clement, C.B., Drain, D., Timcheck, J., Svyatkovskiy, A., Sundaresan, N., 2020 · 2020
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IntelliCode compose: code generation using transformer
Svyatkovskiy, A., Deng, S.K., Fu, S., Sundaresan, N., 2020 · 2020
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Unified pre-training for program understanding and generation, in: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2655–2668
Ahmad, W., Chakraborty, S., Ray, B., Chang, K.W., 2021 · 2021
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Program synthesis with large language models
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C.J., Terry, M., Le, Q.V., Sutton, C., 2021 · 2021
Cited alongside, same era.
GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow
Black, S., Gao, L., Wang, P., Leahy, C., Biderman, S., 2021 · 2021
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Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Ponde, H., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Such, F.P., Cummings, D.W., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W.H., Nichol, A., Babuschkin, I., Balaji, S.A., Jain, S., Carr, A., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M.M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., Zaremba, W., 2021 · 2021
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Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Hilton, J., Nakano, R., Hesse, C., Schulman, J., 2021 · 2021
Jigsaw: Large language models meet program synthesis
Jain, N., Vaidyanath, S., Iyer, A.S., Natarajan, N., Parthasarathy, S., Rajamani, S.K., Sharma, R., 2021 · 2022
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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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Cited alongside, same era.
GPT Code Clippy: The Open Source version of GitHub Copilot
CodedotAl, 2021 · 2021
Cited alongside, same era.
Measuring coding challenge competence with apps, in: Neural Information Processing Systems
Hendrycks, D., Basart, S., Kadavath, S., Mazeika, M., Arora, A., Guo, E., Burns, C., Puranik, S., He, H., Song, D.X., Steinhardt, J., 2021 · 2021
Cited alongside, same era.
Training CodeParrot from Scratch
Huggingface, 2021 · 2021
Cited alongside, same era.
CodeXGLUE: A machine learning benchmark dataset for code understanding and generation
Lu, S., Guo, D., Ren, S., Huang, J., Svyatkovskiy, A., Blanco, A., Clement, C., Drain, D., Jiang, D., Tang, D., et al., 2021 · 2021
Cited alongside, same era.
Retrieval augmented code generation and summarization, in: Findings of EMNLP, pp. 2719–2734
Parvez, M.R., Ahmad, W., Chakraborty, S., Ray, B., Chang, K.W., 2021 · 2021
Cited alongside, same era.
Colbertv2: Effective and efficient retrieval via lightweight late interaction
Santhanam, K., Khattab, O., Saad-Falcon, J., Potts, C., Zaharia, M., 2021 · 2021
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Wang, B., Komatsuzaki, A., 2021 · 2021
Cited alongside, same era.
CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation, in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 8696–8708
Wang, Y., Wang, W., Joty, S., Hoi, S.C., 2021 · 2021
Cited alongside, same era.
Scao, T.L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A.S., Yvon, F., Gallé, M., et al., 2022 · 2022
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Repository-level prompt generation for large language models of code, in: ICML 2022 Workshop on Knowledge Retrieval and Language Models
Shrivastava, D., Larochelle, H., Tarlow, D., 2022 · 2022
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SecurityEval dataset: mining vulnerability examples to evaluate machine learning-based code generation techniques
Siddiq, M.L., msiddiq, 2022 · 2022
Later among the works it cites.
Emergent abilities of large language models
Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., et al., 2022 · 2022
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A systematic evaluation of large language models of code
Xu, F.F., Alon, U., Neubig, G., Hellendoorn, V.J., 2022 · 2022
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CERT: Continual pre-training on sketches for library-oriented code generation, in: The 2022 International Joint Conference on Artificial Intelligence
Zan, D., Chen, B., Yang, D., Lin, Z., Kim, M., Guan, B., Wang, Y., Chen, W., Lou, J.G., 2022b · 2022
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SantaCoder: don’t reach for the stars!
Allal, L.B., Li, R., Kocetkov, D., Mou, C., Akiki, C., Ferrandis, C.M., Muennighoff, N., Mishra, M., Gu, A., Dey, M., Umapathi, L.K., Anderson, C.J., Zi, Y., Poirier, J., Schoelkopf, H., Troshin, S.M., Abulkhanov, D., Romero, M., Lappert, M.F., Toni, F.D., del R’io, B.G., Liu, Q., Bose, S., Bhattacharyya, U., Zhuo, T.Y., Yu, I., Villegas, P., Zocca, M., Mangrulkar, S., Lansky, D., Nguyen, H., Contractor, D., Villa, L., Li, J., Bahdanau, D., Jernite, Y., Hughes, S.C., Fried, D., Guha, A., de Vries, H., von Werra, L., 2023 · 2023
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InCoder: A generative model for code infilling and synthesis, in: The Eleventh International Conference on Learning Representations
Fried, D., Aghajanyan, A., Lin, J., Wang, S., Wallace, E., Shi, F., Zhong, R., Yih, S., Zettlemoyer, L., Lewis, M., 2023 · 2023
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StarCoder: May the source be with you!
Li, R., Allal, L.B., Zi, Y., Muennighoff, N., et al., 2023 · 2023
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Meet in the middle: A new pre-training paradigm
Nguyen, A., Karampatziakis, N., Chen, W., 2023 · 2023
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CodeGen: An open large language model for code with multi-turn program synthesis, in: The Eleventh International Conference on Learning Representations
Nijkamp, E., Pang, B., Hayashi, H., Tu, L., Wang, H., Zhou, Y., Savarese, S., Xiong, C., 2023 · 2023
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OpenAI, 2023 · 2023
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Pangu-coder2: Boosting large language models for code with ranking feedback
Shen, B., Zhang, J., Chen, T., Zan, D., Geng, B., Fu, A., Zeng, M., Yu, A., Ji, J., Zhao, J., Guo, Y., Wang, Q., 2023 · 2023
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Deep learning based code generation methods: A literature review
Yang, Z., Chen, S., Gao, C., Li, Z., Li, G., Lv, R., 2023 · 2023
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Repocoder: Repository-level code completion through iterative retrieval and generation
Zhang, F., Chen, B., Zhang, Y., Liu, J., Zan, D., Mao, Y., Lou, J.G., Chen, W., 2023 · 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., Dong, Y., Wang, S., Xue, Y., Wang, Z.Y., Shen, L., Wang, A., Li, Y., Su, T., Yang, Z., Tang, J., 2023 · 2023
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Zhou, S., Alon, U., Xu, F.F., JIang, Z., Neubig, G., 2023 · 2023
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