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Large language models (LLMs) have recently demonstrated a remarkable ability to generate code from natural language (NL) prompts.
Automating string processing in spreadsheets using input-output examples
Gulwani, S · 2011
Earlier work this paper cites.
Spreadsheet data manipulation using examples
Gulwani, S., Harris, W. R., and Singh, R · 2012
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Syntax-guided synthesis
Alur, R., Bodík, R., Juniwal, G., Martin, M. M. K., Raghothaman, M., Seshia, S. A., Singh, R., Solar-Lezama, A., Torlak, E., and Udupa, A · 2013
Earlier work this paper cites.
Semantic parsing on Freebase from question-answer pairs
Berant, J., Chou, A., Frostig, R., and Liang, P · 2013
Earlier work this paper cites.
Inductive programming meets the real world
Gulwani, S., Hernández-Orallo, J., Kitzelmann, E., Muggleton, S. H., Schmid, U., and Zorn, B · 2015
Earlier work this paper cites.
Deepcoder: Learning to write programs
Balog, M., Gaunt, A. L., Brockschmidt, M., Nowozin, S., and Tarlow, D · 2016
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Programming by examples - and its applications in data wrangling
Gulwani, S · 2016
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Robustfill: Neural program learning under noisy i/o
Devlin, J., Uesato, J., Bhupatiraju, S., Singh, R., rahman Mohamed, A., and Kohli, P · 2017
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Leveraging grammar and reinforcement learning for neural program synthesis
Bunel, R., Hausknecht, M. J., Devlin, J., Singh, R., and Kohli, P · 2018
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Execution-guided neural program synthesis
Chen, X., Liu, C., and Song, D. X · 2018
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Mapping language to code in programmatic context
Iyer, S., Konstas, I., Cheung, A., and Zettlemoyer, L · 2018
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Robust text-to-sql generation with execution-guided decoding
Wang, C., Tatwawadi, K., Brockschmidt, M., Huang, P.-S., Mao, Y., Polozov, O., and Singh, R · 2018
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Write, execute, assess: Program synthesis with a repl
Ellis, K., Nye, M., Pu, Y., Sosa, F., Tenenbaum, J. B., and Solar-Lezama, A · 2019
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Tf-coder: Program synthesis for tensor manipulations
Shi, K., Bieber, D., and Singh, R · 2020
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Autoqa: From databases to q&a semantic parsers with only synthetic training data
Xu, S., Semnani, S. J., Campagna, G., and Lam, M. S · 2020
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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., Terry, M., Le, Q., et al · 2021
Earlier work this paper cites.
Cross-task generalization via natural language crowdsourcing instructions
Mishra, S., Khashabi, D., Baral, C., and Hajishirzi, H · 2021
Cited alongside, same era.
Multitask prompted training enables zero-shot task generalization
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., Dey, M., Bari, M. S., Xu, C., Thakker, U., Sharma, S., Szczechla, E., Kim, T., Chhablani, G., Nayak, N. V., Datta, D., Chang, J., Jiang, M. T.-J., Wang, H., Manica, M., Shen, S., Yong, Z. X., Pandey, H., Bawden, R., Wang, T., Neeraj, T., Rozen, J., Sharma, A., Santilli, A., Févry, T., Fries, J. A., Teehan, R., Biderman, S. R., Gao, L., Bers, T., Wolf, T., and Rush, A. M · 2021
Cited alongside, same era.
Finetuned language models are zero-shot learners
Wei, J., Bosma, M., Zhao, V., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 2021
Cited alongside, same era.
I2d2: Inductive knowledge distillation with neurologic and self-imitation
Bhagavatula, C., Hwang, J. D., Downey, D., Bras, R. L., Lu, X., Sakaguchi, K., Swayamdipta, S., West, P., and Choi, Y · 2022
Guess the instruction! flipped learning makes language models stronger zero-shot learners
Ye, S., Kim, D., Jang, J., Shin, J., and Seo, M · 2022
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On the ingredients of an effective zero-shot semantic parser
Yin, P., Wieting, J. F., Sil, A., and Neubig, G · 2022
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Parsel: A (de-)compositional framework for algorithmic reasoning with language models
Zelikman, E., Huang, Q., Poesia, G., Goodman, N. D., and Haber, N · 2022
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Large language models are human-level prompt engineers
Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., and Ba, J · 2022
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Taking flight with copilot: Early insights and opportunities of ai-powered pair-programming tools
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Cited alongside, same era.
Language models are realistic tabular data generators
Borisov, V., Sessler, K., Leemann, T., Pawelczyk, M., and Kasneci, G · 2022
Cited alongside, same era.
Codet: Code generation with generated tests
Chen, B., Zhang, F., Nguyen, A., Zan, D., Lin, Z., Lou, J.-G., and Chen, W · 2022
Cited alongside, same era.
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., 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
Cited alongside, same era.
Scaling instruction-finetuned language models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, E., Wang, X., Dehghani, M., Brahma, S., Webson, A., Gu, S. S., Dai, Z., Suzgun, M., Chen, X., Chowdhery, A., Valter, D., Narang, S., Mishra, G., Yu, A. W., Zhao, V., Huang, Y., Dai, A. M., Yu, H., Petrov, S., hsin Chi, E. H., Dean, J., Devlin, J., Roberts, A., Zhou, D., Le, Q. V., and Wei, J · 2022
Cited alongside, same era.
Incoder: A generative model for code infilling and synthesis
Fried, D., Aghajanyan, A., Lin, J., Wang, S., Wallace, E., Shi, F., Zhong, R., Yih, W.-t., Zettlemoyer, L., and Lewis, M · 2022
Cited alongside, same era.
Jigsaw: Large language models meet program synthesis
Jain, N., Vaidyanath, S., Iyer, A., Natarajan, N., Parthasarathy, S., Rajamani, S., and Sharma, R · 2022
Cited alongside, same era.
Ds-1000: A natural and reliable benchmark for data science code generation
Lai, Y., Li, C., Wang, Y., Zhang, T., Zhong, R., Zettlemoyer, L., tau Yih, S. W., Fried, D., Wang, S., and Yu, T · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Bird, C., Ford, D., Zimmermann, T., Forsgren, N., Kalliamvakou, E., Lowdermilk, T., and Gazit, I · 2023
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Teaching large language models to self-debug
Chen, X., Lin, M., Schärli, N., and Zhou, D · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E., Stoica, I., and Xing, E. P · 2023
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PaLM2 technical report
Google · 2023
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Josifoski, M., Sakota, M., Peyrard, M., and West, R · 2023
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Starcoder: may the source be with you!
Li, R., Allal, L. B., Zi, Y., Muennighoff, N., Kocetkov, D., Mou, C., Marone, M., Akiki, C., Li, J., Chim, J., Liu, Q., Zheltonozhskii, E., Zhuo, T. Y., Wang, T., Dehaene, O., Davaadorj, M., Lamy-Poirier, J., Monteiro, J., Shliazhko, O., Gontier, N., Meade, N., Zebaze, A., Yee, M.-H., Umapathi, L. K., Zhu, J., Lipkin, B., Oblokulov, M., Wang, Z., Murthy, R., Stillerman, J., Patel, S. S., Abulkhanov, D., Zocca, M., Dey, M., Zhang, Z., Fahmy, N., Bhattacharyya, U., Yu, W., Singh, S., Luccioni, S., Villegas, P., Kunakov, M., Zhdanov, F., Romero, M., Lee, T., Timor, N., Ding, J., Schlesinger, C., Schoelkopf, H., Ebert, J., Dao, T., Mishra, M., Gu, A., Robinson, J., Anderson, C. J., Dolan-Gavitt, B., Contractor, D., Reddy, S., Fried, D., Bahdanau, D., Jernite, Y., Ferrandis, C. M., Hughes, S. M., Wolf, T., Guha, A., von Werra, L., and de Vries, H · 2023
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Peng, B., Li, C., He, P., Galley, M., and Gao, J · 2023
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Code llama: Open foundation models for code
Rozière, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X., Adi, Y., Liu, J., Remez, T., Rapin, J., Kozhevnikov, A., Evtimov, I., Bitton, J., Bhatt, M. P., Ferrer, C. C., Grattafiori, A., Xiong, W., D’efossez, A., Copet, J., Azhar, F., Touvron, H., Martin, L., Usunier, N., Scialom, T., and Synnaeve, G · 2023
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Does synthetic data generation of llms help clinical text mining?
Tang, R., Han, X., Jiang, X., and Hu, X · 2023
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Stanford alpaca: An instruction-following llama model
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., and Hashimoto, T. B · 2023
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Planning with large language models for code generation
Zhang, S., Chen, Z., Shen, Y., Ding, M., Tenenbaum, J. B., and Gan, C · 2023
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