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Current benchmarks for evaluating neural code models focus on only a small subset of programming languages, excluding many popular languages such as Go or Rust.
Kudo, T. and Richardson, J · 2018
Earlier work this paper cites.
Adafactor: Adaptive learning rates with sublinear memory cost
Shazeer, N. and Stern, M · 2018
Earlier work this paper cites.
Learning to mine aligned code and natural language pairs from stack overflow
Yin, P., Deng, B., Chen, E., Vasilescu, B., and Neubig, G · 2018
Earlier work this paper cites.
The adverse effects of code duplication in machine learning models of code
Allamanis, M · 2019
Earlier work this paper cites.
Massively multilingual neural machine translation in the wild: Findings and challenges
Arivazhagan, N., Bapna, A., Firat, O., Lepikhin, D., Johnson, M., Krikun, M., Chen, M. X., Cao, Y., Foster, G., Cherry, C., et al · 2019
Earlier work this paper cites.
Unsupervised cross-lingual representation learning at scale
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., and Stoyanov, V · 2019
Earlier work this paper cites.
Codesearchnet challenge: Evaluating the state of semantic code search
Husain, H., Wu, H., Gazit, T., Allamanis, M., and Brockschmidt, M · 2019
Earlier work this paper cites.
PyMT5: multi-mode translation of natural language and python code with transformers
Clement, C., Drain, D., Timcheck, J., Svyatkovskiy, A., and Sundaresan, N · 2020
Earlier work this paper cites.
CodeBERT: A pre-trained model for programming and natural languages
Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., and Zhou, M · 2020
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P. J., et al · 2020
Earlier work this paper cites.
Unsupervised translation of programming languages
Roziere, B., Lachaux, M.-A., Chanussot, L., and Lample, G · 2020
Earlier work this paper cites.
Balancing training for multilingual neural machine translation
Wang, X., Tsvetkov, Y., and Neubig, G · 2020
Earlier work this paper cites.
Unified pre-training for program understanding and generation
Ahmad, W., Chakraborty, S., Ray, B., and Chang, K.-W · 2021
Earlier work this paper cites.
Program synthesis with large language models
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., et al · 2021
Cited alongside, same era.
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., and Zaremba, W · 2021
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Measuring coding challenge competence with APPS
Hendrycks, D., Basart, S., Kadavath, S., Mazeika, M., Arora, A., Guo, E., Burns, C., Puranik, S., He, H., Song, D., and Steinhardt, J · 2021
Cited alongside, same era.
Codexglue: A machine learning benchmark dataset for code understanding and generation
Pangu-coder: Program synthesis with function-level language modeling
Christopoulou, F., Lampouras, G., Gritta, M., Zhang, G., Guo, Y., Li, Z.-Y., Zhang, Q., Xiao, M., Shen, B., Li, L., Yu, H., yu Yan, L., Zhou, P., Wang, X., Ma, Y., Iacobacci, I., Wang, Y., Liang, G., Wei, J., Jiang, X., Wang, Q., and Liu, Q · 2022
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Incoder: A generative model for code infilling and synthesis
Fried, D., Aghajanyan, A., Lin, J., Wang, S. I., Wallace, E., Shi, F., Zhong, R., tau Yih, W., Zettlemoyer, L., and Lewis, M · 2022
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The stack: 3 tb of permissively licensed source code
Kocetkov, D., Li, R., Allal, L. B., Li, J., Mou, C., Ferrandis, C. M., Jernite, Y., Mitchell, M., Hughes, S., Wolf, T., et al · 2022
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Ds-1000: A natural and reliable benchmark for data science code generation
Lai, Y., Li, C., Wang, Y., Zhang, T., Zhong, R., Zettlemoyer, L., Yih, S., Fried, D., yi Wang, S., and Yu, T · 2022
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Lu, S., Guo, D., Ren, S., Huang, J., Svyatkovskiy, A., Blanco, A., Clement, C. B., Drain, D., Jiang, D., Tang, D., Li, G., Zhou, L., Shou, L., Zhou, L., Tufano, M., Gong, M., Zhou, M., Duan, N., Sundaresan, N., Deng, S. K., Fu, S., and Liu, S · 2021
Cited alongside, same era.
Reading stackoverflow encourages cheating: Adding question text improves extractive code generation
Orlanski, G. and Gittens, A · 2021
Cited alongside, same era.
Dobf: A deobfuscation pre-training objective for programming languages
Rozière, B., Lachaux, M.-A., Szafraniec, M., and Lample, G · 2021
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Programming puzzles
Schuster, T., Kalyan, A., Polozov, A., and Kalai, A. T · 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., and Hoi, S. C · 2021
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Break-it-fix-it: Unsupervised learning for program repair
Yasunaga, M. and Liang, P · 2021
Cited alongside, same era.
Efficient training of language models to fill in the middle
Bavarian, M., Jun, H., Tezak, N., Schulman, J., McLeavey, C., Tworek, J., and Chen, M · 2022
Cited alongside, same era.
A scalable and extensible approach to benchmarking nl2code for 18 programming languages
Cassano, F., Gouwar, J., Nguyen, D., Nguyen, S., Phipps-Costin, L., Pinckney, D., Yee, M. H., Zi, Y., Anderson, C. J., Feldman, M. Q., et al · 2022
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Natgen: generative pre-training by “naturalizing” source code
Chakraborty, S., Ahmed, T., Ding, Y., Devanbu, P., and Ray, B · 2022
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Competition-level code generation with alphacode
Li, Y., Choi, D. H., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Tom, Eccles, Keeling, J., Gimeno, F., Lago, A. D., Hubert, T., Choy, P., de, C., d’Autume, M., Babuschkin, I., Chen, X., Huang, P.-S., Welbl, J., Gowal, S., Alexey, Cherepanov, Molloy, J., Mankowitz, D. J., Robson, E. S., Kohli, P., de, N., Freitas, Kavukcuoglu, K., and Vinyals, O · 2022
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A conversational paradigm for program synthesis
Nijkamp, E., Pang, B., Hayashi, H., Tu, L., Wang, H., Zhou, Y., Savarese, S., and Xiong, C · 2022
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Evaluating how fine-tuning on bimodal data effects code generation
Orlanski, G., Yang, S., and Healy, M · 2022
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Scaling up models and data with t5x
Roberts, A., Chung, H. W., Levskaya, A., Mishra, G., Bradbury, J., Andor, D., Narang, S., Lester, B., Gaffney, C., Mohiuddin, A., Hawthorne, C., Lewkowycz, A., Salcianu, A., van Zee, M., Austin, J., Goodman, S., Soares, L. B., Hu, H., Tsvyashchenko, S., Chowdhery, A., Bastings, J., Bulian, J., Garcia, X., Ni, J., Chen, A., Kenealy, K., Clark, J. H., Lee, S., Garrette, D., Lee-Thorp, J., Raffel, C., Shazeer, N., Ritter, M., Bosma, M., Passos, A., Maitin-Shepard, J., Fiedel, N., Omernick, M., Saeta, B., Sepassi, R., Spiridonov, A., Newlan, J., and Gesmundo, A · 2022
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Unifying language learning paradigms
Tay, Y., Dehghani, M., Tran, V. Q., Garcia, X., Bahri, D., Schuster, T., Zheng, H. S., Houlsby, N., and Metzler, D · 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., et al · 2023
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Multi-lingual evaluation of code generation models
Athiwaratkun, B., Gouda, S. K., Wang, Z., Li, X., Tian, Y., Tan, M., Ahmad, W. U., Wang, S., Sun, Q., Shang, M., Gonugondla, S. K., Ding, H., Kumar, V., Fulton, N., Farahani, A., Jain, S., Giaquinto, R., Qian, H., Ramanathan, M. K., Nallapati, R., Ray, B., Bhatia, P., Sengupta, S., Roth, D., and Xiang, B · 2023
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Unimax: Fairer and more effective language sampling for large-scale multilingual pretraining
Chung, H. W., Garcia, X., Roberts, A., Tay, Y., Firat, O., Narang, S., and Constant, N · 2023
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