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Neural Code Intelligence -- leveraging deep learning to understand, generate, and optimize code -- holds immense potential for transformative impacts on the whole society.
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C. Lee, O. Polozov, and M. Richardson, “Kaggledbqa: Realistic evaluation of text-to-sql parsers,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021 , C. Zong, F. Xia, W. Li, and R. Navigli, Eds. Association for Computational Linguistics, 2021, pp. 2261–2273. [Online]. Available: https://doi.org/10.18653/v1/2021.acl-long.176
2021
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X. Chen, L. Gong, A. Cheung, and D. Song, “Plotcoder: Hierarchical decoding for synthesizing visualization code in programmatic context,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , 2021
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T. Sun, X. Liu, X. Qiu, and X. Huang, “Paradigm shift in natural language processing,” Machine Intelligence Research , vol. 19, pp. 169–183, 2022
2022
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J. Hoffmann, S. Borgeaud, A. Mensch, E. Buchatskaya, T. Cai, E. Rutherford, D. de las Casas, L. A. Hendricks, J. Welbl, A. Clark, T. Hennigan, E. Noland, K. Millican, G. van den Driessche, B. Damoc, A. Guy, S. Osindero, K. Simonyan, E. Elsen, O. Vinyals, J. W. Rae, and L. Sifre, “An empirical analysis of compute-optimal large language model training,” in Advances in Neural Information Processing Systems , A. H. Oh, A. Agarwal, D. Belgrave, and K. Cho, Eds., 2022. [Online]. Available: https://openreview.net/forum?id=iBBcRUlOAPR
2022
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Z. Bi, N. Zhang, Y. Jiang, S. Deng, G. Zheng, and H. Chen, “When do program-of-thoughts work for reasoning?” 2023
2023
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H. Luo, Q. Sun, C. Xu, P. Zhao, J. Lou, C. Tao, X. Geng, Q. Lin, S. Chen, and D. Zhang, “Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct,” 2023
2023
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C. Qin, A. Zhang, Z. Zhang, J. Chen, M. Yasunaga, and D. Yang, “Is ChatGPT a general-purpose natural language processing task solver?” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing . Singapore: Association for Computational Linguistics, Dec. 2023, pp. 1339–1384. [Online]. Available: https://aclanthology.org/2023.emnlp-main.85
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Y. Ma, Y. Liu, Y. Yu, Y. Zhang, Y. Jiang, C. Wang, and S. Li, “At which training stage does code data help llms reasoning?” 2023
2023
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D. Xu, W. Chen, W. Peng, C. Zhang, T. Xu, X. Zhao, X. Wu, Y. Zheng, and E. Chen, “Large language models for generative information extraction: A survey,” 2023
2023
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X. Wang, S. Li, and H. Ji, “Code4Struct: Code generation for few-shot event structure prediction,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Toronto, Canada: Association for Computational Linguistics, Jul. 2023, pp. 3640–3663. [Online]. Available: https://aclanthology.org/2023.acl-long.202
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P. Li, T. Sun, Q. Tang, H. Yan, Y. Wu, X. Huang, and X. Qiu, “CodeIE: Large code generation models are better few-shot information extractors,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Toronto, Canada: Association for Computational Linguistics, Jul. 2023, pp. 15 339–15 353. [Online]. Available: https://aclanthology.org/2023.acl-long.855
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Y. Guo, Z. Li, X. Jin, Y. Liu, Y. Zeng, W. Liu, X. Li, P. Yang, L. Bai, J. Guo, and X. Cheng, “Retrieval-augmented code generation for universal information extraction,” 2023
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L. Logeswaran, S. Sohn, Y. Lyu, A. Z. Liu, D.-K. Kim, D. Shim, M. Lee, and H. Lee, “Code models are zero-shot precondition reasoners,” 2023
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Y. Dong, L. Martin, and C. Callison-Burch, “CoRRPUS: Code-based structured prompting for neurosymbolic story understanding,” in Findings of the Association for Computational Linguistics: ACL 2023 . Toronto, Canada: Association for Computational Linguistics, Jul. 2023, pp. 13 152–13 168. [Online]. Available: https://aclanthology.org/2023.findings-acl.832
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T. Schick, J. Dwivedi-Yu, R. Dessi, R. Raileanu, M. Lomeli, E. Hambro, L. Zettlemoyer, N. Cancedda, and T. Scialom, “Toolformer: Language models can teach themselves to use tools,” in Thirty-seventh Conference on Neural Information Processing Systems , 2023. [Online]. Available: https://openreview.net/forum?id=Yacmpz84TH
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J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng, “Code as policies: Language model programs for embodied control,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 9493–9500
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