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The advent of large language models trained on code (code LLMs) has led to significant progress in language-to-code generation.
Roberta: A robustly optimized BERT pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 1907
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Progress in natural language understanding: an application to lunar geology
Woods, W. A · 1973
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Problems in natural-language interface to DSMS with examples from EUFID
Templeton, M. and Burger, J. D · 1983
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Learning to parse database queries using inductive logic programming
Zelle, J. M. and Mooney, R. J · 1996
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Discriminative reranking for machine translation
Shen, L., Sarkar, A., and Och, F. J · 2004
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Discriminative reranking for natural language parsing
Collins, M. and Koo, T · 2005
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Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars
Zettlemoyer, L. S. and Collins, M · 2005
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Deberta: Decoding-enhanced bert with disentangled attention, 2020
He, P., Liu, X., Gao, J., and Chen, W · 2006
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Unit test case generation with transformers
Tufano, M., Drain, D., Svyatkovskiy, A., Deng, S. K., and Sundaresan, N · 2009
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Weakly supervised learning of semantic parsers for mapping instructions to actions
Artzi, Y. and Zettlemoyer, L · 2013
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Semantic parsing on Freebase from question-answer pairs
Berant, J., Chou, A., Frostig, R., and Liang, P · 2013
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Nlyze: interactive programming by natural language for spreadsheet data analysis and manipulation
Gulwani, S. and Marron, M · 2014
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Compositional semantic parsing on semi-structured tables
Pasupat, P. and Liang, P · 2015
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Formal Verification: An Essential Toolkit for Modern VLSI Design
Seligman, E., Schubert, T., and Kumar, M. V. A. K · 2015
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Multi-document summarization via discriminative summary reranking
Wan, X., Cao, Z., Wei, F., Li, S., and Zhou, M · 2015
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Language to logical form with neural attention
Dong, L. and Lapata, M · 2016
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Sequence-based structured prediction for semantic parsing
Xiao, C., Dymetman, M., and Gardent, C · 2016
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From language to programs: Bridging reinforcement learning and maximum marginal likelihood
Guu, K., Pasupat, P., Liu, E. Z., and Liang, P · 2017
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Program synthesis from natural language using recurrent neural networks
Lin, X. V., Wang, C., Pang, D., Vu, K., Zettlemoyer, L., and Ernst, M. D · 2017
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Abstract syntax networks for code generation and semantic parsing
Rabinovich, M., Stern, M., and Klein, D · 2017
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Seq2sql: Generating structured queries from natural language using reinforcement learning
Zhong, V., Xiong, C., and Socher, R · 2017
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Weakly-supervised neural semantic parsing with a generative ranker
Cheng, J. and Lapata, M · 2018
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Weakly supervised semantic parsing with abstract examples
Goldman, O., Latcinnik, V., Nave, E., Globerson, A., and Berant, J · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Multi-turn dialogue response generation in an adversarial learning framework
Olabiyi, O., Salimov, A., Khazane, A., and Mueller, E. T · 2018
Cited alongside, same era.
Robust text-to-sql generation with execution-guided decoding, 2018
Wang, C., Tatwawadi, K., Brockschmidt, M., Huang, P.-S., Mao, Y., Polozov, O., and Singh, R · 2018
Cited alongside, same era.
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
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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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
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Hierarchical control of situated agents through natural language
Zhou, S., Yin, P., and Neubig, G · 2021
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Binding language models in symbolic languages
Cheng, Z., Xie, T., Shi, P., Li, C., Nadkarni, R., Hu, Y., Xiong, C., Radev, D., Ostendorf, M., Zettlemoyer, L., et al · 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., et al · 2022
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Cited alongside, same era.
JuICe: A large scale distantly supervised dataset for open domain context-based code generation
Agashe, R., Iyer, S., and Zettlemoyer, L · 2019
Cited alongside, same era.
Execution-guided neural program synthesis
Chen, X., Liu, C., and Song, D · 2019
Cited alongside, same era.
BERT: pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M., Lee, K., and Toutanova, K · 2019
Cited alongside, same era.
Reranking for neural semantic parsing
Yin, P. and Neubig, G · 2019
Cited alongside, same era.
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
Cited alongside, same era.
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., and Liu, P. J · 2020
Cited alongside, same era.
On the potential of lexico-logical alignments for semantic parsing to SQL queries
Shi, T., Zhao, C., Boyd-Graber, J., Daumé III, H., and Lee, L · 2020
Cited alongside, same era.
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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
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Pal: Program-aided language models
Gao, L., Madaan, A., Zhou, S., Alon, U., Liu, P., Yang, Y., Callan, J., and Neubig, G · 2022
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Omnitab: Pretraining with natural and synthetic data for few-shot table-based question answering
Jiang, Z., Mao, Y., He, P., Neubig, G., and Chen, W · 2022
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Language models (mostly) know what they know, 2022
Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., Schiefer, N., Hatfield-Dodds, Z., DasSarma, N., Tran-Johnson, E., Johnston, S., El-Showk, S., Jones, A., Elhage, N., Hume, T., Chen, A., Bai, Y., Bowman, S., Fort, S., Ganguli, D., Hernandez, D., Jacobson, J., Kernion, J., Kravec, S., Lovitt, L., Ndousse, K., Olsson, C., Ringer, S., Amodei, D., Brown, T., Clark, J., Joseph, N., Mann, B., McCandlish, S., Olah, C., and Kaplan, J · 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. W.-t., Fried, D., Wang, S., and Yu, T · 2022
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Learning from self-sampled correct and partially-correct programs
Ni, A., Inala, J. P., Wang, C., Polozov, O., Meek, C., Radev, D., and Gao, J · 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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Chatgpt: Optimizing language models for dialogue, November 2022
OpenAI · 2022
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Rasat: Integrating relational structures into pretrained seq2seq model for text-to-sql
Qi, J., Tang, J., He, Z., Wan, X., Zhou, C., Wang, X., Zhang, Q., and Lin, Z · 2022
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Evaluating the text-to-sql capabilities of large language models
Rajkumar, N., Li, R., and Bahdanau, D · 2022
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Natural language to code translation with execution
Shi, F., Fried, D., Ghazvininejad, M., Zettlemoyer, L., and Wang, S. I · 2022
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Self-consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., and Zhou, D · 2022
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Generating sequences by learning to self-correct
Welleck, S., Lu, X., West, P., Brahman, F., Shen, T., Khashabi, D., and Choi, Y · 2022
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Xie, T., Wu, C. H., Shi, P., Zhong, R., Scholak, T., Yasunaga, M., Wu, C.-S., Zhong, M., Yin, P., Wang, S. I., et al · 2022
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Coder reviewer reranking for code generation
Zhang, T., Yu, T., Hashimoto, T. B., Lewis, M., Yih, W.-t., Fried, D., and Wang, S. I · 2022
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Tacube: Pre-computing data cubes for answering numerical-reasoning questions over tabular data
Zhou, F., Hu, M., Dong, H., Cheng, Z., Han, S., and Zhang, D · 2022
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