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Sampling diverse programs from a code language model and reranking with model likelihood is a popular method for code generation but it is prone to preferring degenerate solutions.
Maximum mutual information estimation of hidden markov model parameters for speech recognition
Bahl, L. R., Brown, P. F., de Souza, P. V., and Mercer, R. L · 1986
Earlier work this paper cites.
Suggesting accurate method and class names
Allamanis, M., Barr, E. T., Bird, C., and Sutton, C · 2015
Earlier work this paper cites.
A diversity-promoting objective function for neural conversation models
Li, J., Galley, M., Brockett, C., Gao, J., and Dolan, B · 2016
Earlier work this paper cites.
Latent predictor networks for code generation
Ling, W., Blunsom, P., Grefenstette, E., Hermann, K. M., Kočiský, T., Wang, F., and Senior, A · 2016
Earlier work this paper cites.
Active programming by example with a natural language prior
Zhong, R., Snell, C., Klein, D., and Eisner, J · 2016
Earlier work this paper cites.
A syntactic neural model for general-purpose code generation
Yin, P. and Neubig, G · 2017
Earlier work this paper cites.
Speaker-follower models for vision-and-language navigation
Fried, D., Hu, R., Cirik, V., Rohrbach, A., Andreas, J., Morency, L.-P., Berg-Kirkpatrick, T., Saenko, K., Klein, D., and Darrell, T · 2018
Earlier work this paper cites.
Mapping language to code in programmatic context
Iyer, S., Konstas, I., Cheung, A., and Zettlemoyer, L · 2018
Earlier work this paper cites.
NL2Bash: A corpus and semantic parser for natural language interface to the linux operating system
Lin, X. V., Wang, C., Zettlemoyer, L., and Ernst, M. D · 2018
Earlier work this paper cites.
Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task
Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I., Yao, Q., Roman, S., Zhang, Z., and Radev, D · 2018
Earlier work this paper cites.
Generative question answering: Learning to answer the whole question
Lewis, M. and Fan, A · 2019
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On nmt search errors and model errors: Cat got your tongue?
Stahlberg, F. and Byrne, B · 2019
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Reranking for neural semantic parsing
Yin, P. and Neubig, G · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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The curious case of neural text degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., and Choi, Y · 2020
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Graph-based, self-supervised program repair from diagnostic feedback
Yasunaga, M. and Liang, P · 2020
Codet: Code generation with generated tests
Chen, B., Zhang, F., Nguyen, A., Zan, D., Lin, Z., Lou, J.-G., and Chen, W · 2022
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Palm: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A. B., Barnes, P., Tay, Y., Shazeer, N. M., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B. C., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., García, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A. M., Pillai, T. S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Díaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K. S., Eck, D., Dean, J., Petrov, S., and Fiedel, N · 2022
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Incoder: A generative model for code infilling and synthesis
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Program synthesis with large language models
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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., 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
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Noisy channel language model prompting for few-shot text classification
Min, S., Lewis, M., Hajishirzi, H., and Zettlemoyer, L · 2021
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Inala, J. P., Wang, C., Yang, M., Codas, A., Encarnaci’on, M., Lahiri, S. K., Musuvathi, M., and Gao, J · 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
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Lago, A. D., et al · 2022
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Codegen: An open large language model for code with multi-turn program synthesis
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Natural language to code translation with execution
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Guess the instruction! flipped learning makes language models stronger zero-shot learners
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