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LLMs have demonstrated significant potential in code generation tasks, achieving promising results at the function or statement level across various benchmarks.
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.
Realm: retrieval-augmented language model pre-training
Guu, K., Lee, K., Tung, Z., Pasupat, P., and Chang, M.-W · 2020
Earlier work this paper cites.
Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H. P., 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., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W. H., Nichol, A., Paino, A., Tezak, N., Tang, J., Babuschkin, I., Balaji, S., Jain, S., Saunders, W., Hesse, C., Carr, A. N., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., and Zaremba, W · 2021
Earlier work this paper cites.
Program synthesis with large language models
Odena, A., Sutton, C., Dohan, D. M., Jiang, E., Michalewski, H., Austin, J., Bosma, M. P., Nye, M., Terry, M., and Le, Q. V · 2021
Earlier work this paper cites.
Joint retrieval and generation training for grounded text generation
Zhang, Y., Sun, S., Gao, X., Fang, Y., Brockett, C., Galley, M., Gao, J., and Dolan, B · 2021
Earlier work this paper cites.
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 · 2022
Earlier work this paper cites.
Unixcoder: Unified cross-modal pre-training for code representation, 2022
Guo, D., Lu, S., Duan, N., Wang, Y., Zhou, M., and Yin, J · 2022
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Few-shot Learning with Retrieval Augmented Language Models
Izacard, G., Lewis, P., Lomeli, M., Hosseini, L., Petroni, F., Schick, T., Dwivedi-Yu, J., Joulin, A., Riedel, S., and Grave, E · 2022
Earlier work this paper cites.
Monitor-guided decoding of code LMs with static analysis of repository context
Agrawal, L., Kanade, A., Goyal, N., Lahiri, S. K., and Rajamani, S · 2023
Earlier work this paper cites.
Classeval: A manually-crafted benchmark for evaluating llms on class-level code generation, 2023
Du, X., Liu, M., Wang, K., Wang, H., Liu, J., Chen, Y., Feng, J., Sha, C., Peng, X., and Lou, Y · 2023
Cited alongside, same era.
Active retrieval augmented generation
Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., and Neubig, G · 2023
Cited alongside, same era.
Starcoder: may the source be with you!, 2023
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., Wolf, T., Guha, A., von Werra, L., and de Vries, H · 2023
Cited alongside, same era.
Repobench: Benchmarking repository-level code auto-completion systems, 2023
Liu, T., Xu, C., and McAuley, J · 2023
Cited alongside, same era.
Repository-level prompt generation for large language models of code, 2023
Shrivastava, D., Larochelle, H., and Tarlow, D · 2023
Later among the works it cites.
React: Synergizing reasoning and acting in language models
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K. R., and Cao, Y · 2023
Later among the works it cites.
RepoCoder: Repository-level code completion through iterative retrieval and generation
Zhang, F., Chen, B., Zhang, Y., Keung, J., Liu, J., Zan, D., Mao, Y., Lou, J.-G., and Chen, W · 2023
Later among the works it cites.
Language agent tree search unifies reasoning acting and planning in language models, 2023
Zhou, A., Yan, K., Shlapentokh-Rothman, M., Wang, H., and Wang, Y.-X · 2023
Later among the works it cites.
Guo, D., Zhu, Q., Yang, D., Xie, Z., Dong, K., Zhang, W., Chen, G., Bi, X., Wu, Y., Li, Y., Luo, F., Xiong, Y., and Liang, W · 2024
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Wizardcoder: Empowering code large language models with evol-instruct, 2023
Luo, Z., Xu, C., Zhao, P., Sun, Q., Geng, X., Hu, W., Tao, C., Ma, J., Lin, Q., and Jiang, D · 2023
Cited alongside, same era.
Self-refine: Iterative refinement with self-feedback, 2023
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., Welleck, S., Majumder, B. P., Gupta, S., Yazdanbakhsh, A., and Clark, P · 2023
Cited alongside, same era.
Codegen: An open large language model for code with multi-turn program synthesis
Nijkamp, E., Pang, B., Hayashi, H., Tu, L., Wang, H., Zhou, Y., Savarese, S., and Xiong, C · 2023
Cited alongside, same era.
Toolformer: Language models can teach themselves to use tools, 2023
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., and Scialom, T · 2023
Cited alongside, same era.
Reflexion: Language agents with verbal reinforcement learning, 2023
Shinn, N., Cassano, F., Berman, E., Gopinath, A., Narasimhan, K., and Yao, S · 2023
Cited alongside, same era.
SWE-bench: Can language models resolve real-world github issues?
Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., and Narasimhan, K. R · 2024
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Code llama: Open foundation models for code, 2024
Rozière, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X. E., Adi, Y., Liu, J., Sauvestre, R., Remez, T., Rapin, J., Kozhevnikov, A., Evtimov, I., Bitton, J., Bhatt, M., Ferrer, C. C., Grattafiori, A., Xiong, W., Défossez, A., Copet, J., Azhar, F., Touvron, H., Martin, L., Usunier, N., Scialom, T., and Synnaeve, G · 2024
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Learning-based widget matching for migrating gui test cases
Zhang, Y., Zhang, W., Ran, D., Zhu, Q., Dou, C., Hao, D., Xie, T., and Zhang, L · 2024
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