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Code completion models have made significant progress in recent years, yet current popular evaluation datasets, such as HumanEval and MBPP, predominantly focus on code completion tasks within a single file.
Codesearchnet challenge: Evaluating the state of semantic code search
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Transformers: State-of-the-art natural language processing, in: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Association for Computational Linguistics, Online. pp. 38–45
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GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow URL: https://doi.org/10.5281/zenodo.5297715 , doi: 10.5281/zenodo.5297715
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Evaluating large language models trained on code
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Long-range modeling of source code files with eWASH: Extended window access by syntax hierarchy, in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Online and Punta Cana, Dominican Republic. pp. 4713–4722
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Measuring coding challenge competence with APPS, in: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)
Hendrycks, D., Basart, S., Kadavath, S., Mazeika, M., Arora, A., Guo, E., Burns, C., Puranik, S., He, H., Song, D., Steinhardt, J., 2021 · 2021
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CodeXGLUE: A machine learning benchmark dataset for code understanding and generation, in: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)
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 · 2022
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ReACC: A retrieval-augmented code completion framework, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Dublin, Ireland. pp. 6227–6240
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Training language models to follow instructions with human feedback, in: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc.. pp. 27730–27744
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Lu, S., Guo, D., Ren, S., Huang, J., Svyatkovskiy, A., Blanco, A., Clement, C., Drain, D., Jiang, D., Tang, D., Li, G., Zhou, L., Shou, L., Zhou, L., Tufano, M., GONG, M., Zhou, M., Duan, N., Sundaresan, N., Deng, S.K., Fu, S., LIU, S., 2021 · 2021
Cited alongside, same era.
Codenet: A large-scale AI for code dataset for learning a diversity of coding tasks, in: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)
Puri, R., Kung, D.S., Janssen, G., Zhang, W., Domeniconi, G., Zolotov, V., Dolby, J., Chen, J., Choudhury, M., Decker, L., Thost, V., Buratti, L., Pujar, S., Ramji, S., Finkler, U., Malaika, S., Reiss, F., 2021 · 2021
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Wang, B., Komatsuzaki, A., 2021 · 2021
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CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation, in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Online and Punta Cana, Dominican Republic. pp. 8696–8708
Wang, Y., Wang, W., Joty, S., Hoi, S.C., 2021 · 2021
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Efficient training of language models to fill in the middle
Bavarian, M., Jun, H., Tezak, N., Schulman, J., McLeavey, C., Tworek, J., Chen, M., 2022 · 2022
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GPT-NeoX-20B: An open-source autoregressive language model, in: Proceedings of BigScience Episode #5 – Workshop on Challenges & Perspectives in Creating Large Language Models, Association for Computational Linguistics, virtual+Dublin. pp. 95–136
Black, S., Biderman, S., Hallahan, E., Anthony, Q., Gao, L., Golding, L., He, H., Leahy, C., McDonell, K., Phang, J., Pieler, M., Prashanth, U.S., Purohit, S., Reynolds, L., Tow, J., Wang, B., Weinbach, S., 2022 · 2022
Cited alongside, same era.
https://github.com/THUDM/CodeGeeX
CodeGeeX, 2022 · 2022
Cited alongside, same era.
Cocomic: Code completion by jointly modeling in-file and cross-file context
Ding, Y., Wang, Z., Ahmad, W.U., Ramanathan, M.K., Nallapati, R., Bhatia, P., Roth, D., Xiang, B., 2022 · 2022
Cited alongside, same era.
Unixcoder: Unified cross-modal pre-training for code representation, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7212–7225
Guo, D., Lu, S., Duan, N., Wang, Y., Zhou, M., Yin, J., 2022 · 2022
Cited alongside, same era.
A systematic evaluation of large language models of code, in: Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming, Association for Computing Machinery, New York, NY, USA. p. 1–10
Xu, F.F., Alon, U., Neubig, G., Hellendoorn, V.J., 2022 · 2022
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Santacoder: don’t reach for the stars!
Allal, L.B., Li, R., Kocetkov, D., Mou, C., Akiki, C., Ferrandis, C.M., Muennighoff, N., Mishra, M., Gu, A., Dey, M., et al., 2023 · 2023
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Multi-lingual evaluation of code generation models, in: The Eleventh International Conference on Learning Representations
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., Xiang, B., 2023 · 2023
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Multipl-e: A scalable and polyglot approach to benchmarking neural code generation
Cassano, F., Gouwar, J., Nguyen, D., Nguyen, S., Phipps-Costin, L., Pinckney, D., Yee, M.H., Zi, Y., Anderson, C.J., Feldman, M.Q., Guha, A., Greenberg, M., Jangda, A., 2023 · 2023
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A static evaluation of code completion by large language models
Ding, H., Kumar, V., Tian, Y., Wang, Z., Kwiatkowski, R., Li, X., Ramanathan, M.K., Ray, B., Bhatia, P., Sengupta, S., et al., 2023 · 2023
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Incoder: A generative model for code infilling and synthesis, in: The Eleventh International Conference on Learning Representations
Fried, D., Aghajanyan, A., Lin, J., Wang, S., Wallace, E., Shi, F., Zhong, R., Yih, S., Zettlemoyer, L., Lewis, M., 2023 · 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., et al., 2023 · 2023
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Better context makes better code language models: A case study on function call argument completion, in: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence, AAAI Press
Pei, H., Zhao, J., Lausen, L., Zha, S., Karypis, G., 2023 · 2023
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Repository-level prompt generation for large language models of code, in: Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S., Scarlett, J. (Eds.), Proceedings of the 40th International Conference on Machine Learning, PMLR. pp. 31693–31715
Shrivastava, D., Larochelle, H., Tarlow, D., 2023 · 2023
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ReCode: Robustness evaluation of code generation models, in: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Toronto, Canada. pp. 13818–13843
Wang, S., Li, Z., Qian, H., Yang, C., Wang, Z., Shang, M., Kumar, V., Tan, S., Ray, B., Bhatia, P., Nallapati, R., Ramanathan, M.K., Roth, D., Xiang, B., 2023 · 2023
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Repocoder: Repository-level code completion through iterative retrieval and generation
Zhang, F., Chen, B., Zhang, Y., Liu, J., Zan, D., Mao, Y., Lou, J.G., Chen, W., 2023 · 2023
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