Fetching the paper…
Reading the bibliography…
Code large language models (LLMs) face limitations in repository-level code generation due to their lack of awareness of repository-level dependencies (e.g., user-defined attributes), resulting in dependency errors such as undefined-variable and no-member errors.
1909
Earlier work this paper cites.
K. Papineni, S. Roukos, T. Ward, and W. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, July 6-12, 2002, Philadelphia, PA, USA . ACL, 2002, pp. 311–318. [Online]. Available: https://aclanthology.org/P02-1040/
2002
Earlier work this paper cites.
S. Bengio, O. Vinyals, N. Jaitly, and N. Shazeer, “Scheduled sampling for sequence prediction with recurrent neural networks,” in Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada , C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, Eds., 2015, pp. 1171–1179. [Online]. Available: https://proceedings.neurips.cc/paper/2015/hash/e995f98d56967d946471af29d7bf99f1-Abstract.html
2015
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA , I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, and R. Garnett, Eds., 2017, pp. 5998–6008. [Online]. Available: https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
2017
Earlier work this paper cites.
R. Paulus, C. Xiong, and R. Socher, “A deep reinforced model for abstractive summarization,” in 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings . OpenReview.net, 2018. [Online]. Available: https://openreview.net/forum?id=HkAClQgA-
2018
Earlier work this paper cites.
H. Chen, Y. Huang, Z. Liu, X. Chen, F. Zhou, and X. Luo, “Automatically detecting the scopes of source code comments,” J. Syst. Softw. , vol. 153, pp. 45–63, 2019. [Online]. Available: https://doi.org/10.1016/j.jss.2019.03.010
2019
Earlier work this paper cites.
A. Svyatkovskiy, S. K. Deng, S. Fu, and N. Sundaresan, “Intellicode compose: code generation using transformer,” in ESEC/FSE ’20: 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Virtual Event, USA, November 8-13, 2020 , P. Devanbu, M. B. Cohen, and T. Zimmermann, Eds. ACM, 2020, pp. 1433–1443. [Online]. Available: https://doi.org/10.1145/3368089.3417058
2020
Earlier work this paper cites.
S. Black, L. Gao, P. Wang, C. Leahy, and S. Biderman, “GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow,” Mar. 2021, If you use this software, please cite it using these metadata. [Online]. Available: https://doi.org/10.5281/zenodo.5297715
2021
Earlier work this paper cites.
B. Wang and A. Komatsuzaki, “GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model,” https://github.com/kingoflolz/mesh-transformer-jax , May 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
Y. Wang, W. Wang, S. R. Joty, and S. C. H. Hoi, “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, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021 , M. Moens, X. Huang, L. Specia, and S. W. Yih, Eds. Association for Computational Linguistics, 2021, pp. 8696–8708. [Online]. Available: https://doi.org/10.18653/v1/2021.emnlp-main.685
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
S. Lu, D. Guo, S. Ren, J. Huang, A. Svyatkovskiy, A. Blanco, C. B. Clement, D. Drain, D. Jiang, D. Tang, G. Li, L. Zhou, L. Shou, L. Zhou, M. Tufano, M. Gong, M. Zhou, N. Duan, N. Sundaresan, S. K. Deng, S. Fu, and S. Liu, “Codexglue: A machine learning benchmark dataset for code understanding and generation,” in Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021, December 2021, virtual , J. Vanschoren and S. Yeung, Eds., 2021. [Online]. Available: https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/c16a5320fa475530d9583c34fd356ef5-Abstract-round1.html
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
P. Vaithilingam, T. Zhang, and E. L. Glassman, “Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models,” in CHI ’22: CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 29 April 2022 - 5 May 2022, Extended Abstracts , S. D. J. Barbosa, C. Lampe, C. Appert, and D. A. Shamma, Eds. ACM, 2022, pp. 332:1–332:7. [Online]. Available: https://doi.org/10.1145/3491101.3519665
2022
Earlier work this paper cites.
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” in The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022 . OpenReview.net, 2022. [Online]. Available: https://openreview.net/forum?id=nZeVKeeFYf9
2022
Cited alongside, same era.
X. Wang, Y. Wang, Y. Wan, F. Mi, Y. Li, P. Zhou, J. Liu, H. Wu, X. Jiang, and Q. Liu, “Compilable neural code generation with compiler feedback,” in Findings of the Association for Computational Linguistics: ACL 2022, Dublin, Ireland, May 22-27, 2022 , S. Muresan, P. Nakov, and A. Villavicencio, Eds. Association for Computational Linguistics, 2022, pp. 9–19. [Online]. Available: https://doi.org/10.18653/v1/2022.findings-acl.2
2022
Cited alongside, same era.
K. Arora, L. E. Asri, H. Bahuleyan, and J. C. K. Cheung, “Why exposure bias matters: An imitation learning perspective of error accumulation in language generation,” in Findings of the Association for Computational Linguistics: ACL 2022, Dublin, Ireland, May 22-27, 2022 , S. Muresan, P. Nakov, and A. Villavicencio, Eds. Association for Computational Linguistics, 2022, pp. 700–710. [Online]. Available: https://doi.org/10.18653/v1/2022.findings-acl.58
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Wei, C. S. Xia, and L. Zhang, “Copiloting the copilots: Fusing large language models with completion engines for automated program repair,” in Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2023, San Francisco, CA, USA, December 3-9, 2023 , S. Chandra, K. Blincoe, and P. Tonella, Eds. ACM, 2023, pp. 172–184. [Online]. Available: https://doi.org/10.1145/3611643.3616271
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022
Cited alongside, same era.
M. Komeili, K. Shuster, and J. Weston, “Internet-augmented dialogue generation,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022 , S. Muresan, P. Nakov, and A. Villavicencio, Eds. Association for Computational Linguistics, 2022, pp. 8460–8478. [Online]. Available: https://doi.org/10.18653/v1/2022.acl-long.579
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
K. Shuster, M. Komeili, L. Adolphs, S. Roller, A. Szlam, and J. Weston, “Language models that seek for knowledge: Modular search & generation for dialogue and prompt completion,” in Findings of the Association for Computational Linguistics: EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022 , Y. Goldberg, Z. Kozareva, and Y. Zhang, Eds. Association for Computational Linguistics, 2022, pp. 373–393. [Online]. Available: https://doi.org/10.18653/v1/2022.findings-emnlp.27
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Y. Wang, H. Le, A. Gotmare, N. D. Q. Bui, J. Li, and S. C. H. Hoi, “Codet5+: Open code large language models for code understanding and generation,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023 , H. Bouamor, J. Pino, and K. Bali, Eds. Association for Computational Linguistics, 2023, pp. 1069–1088. [Online]. Available: https://aclanthology.org/2023.emnlp-main.68
2023
Cited alongside, same era.
D. Fried, A. Aghajanyan, J. Lin, S. Wang, E. Wallace, F. Shi, R. Zhong, S. Yih, L. Zettlemoyer, and M. Lewis, “Incoder: A generative model for code infilling and synthesis,” in The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . OpenReview.net, 2023. [Online]. Available: https://openreview.net/pdf?id=hQwb-lbM6EL
2023
Cited alongside, same era.
E. Nijkamp, B. Pang, H. Hayashi, L. Tu, H. Wang, Y. Zhou, S. Savarese, and C. Xiong, “Codegen: An open large language model for code with multi-turn program synthesis,” in The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . OpenReview.net, 2023. [Online]. Available: https://openreview.net/pdf?id=iaYcJKpY2B_
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
Y. Huang, H. Guo, X. Ding, J. Shu, X. Chen, X. Luo, Z. Zheng, and X. Zhou, “A comparative study on method comment and inline comment,” ACM Trans. Softw. Eng. Methodol. , vol. 32, no. 5, pp. 126:1–126:26, 2023. [Online]. Available: https://doi.org/10.1145/3582570
2023
Later among the works it cites.
F. Zhang, B. Chen, Y. Zhang, J. Keung, J. Liu, D. Zan, Y. Mao, J. Lou, and W. Chen, “Repocoder: Repository-level code completion through iterative retrieval and generation,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023 , H. Bouamor, J. Pino, and K. Bali, Eds. Association for Computational Linguistics, 2023, pp. 2471–2484. [Online]. Available: https://doi.org/10.18653/v1/2023.emnlp-main.151
2023
Later among the works it cites.
D. Shrivastava, H. Larochelle, and D. Tarlow, “Repository-level prompt generation for large language models of code,” in International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA , ser. Proceedings of Machine Learning Research, A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, and J. Scarlett, Eds., vol. 202. PMLR, 2023, pp. 31 693–31 715. [Online]. Available: https://proceedings.mlr.press/v202/shrivastava23a.html
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
L. Gao, A. Madaan, S. Zhou, U. Alon, P. Liu, Y. Yang, J. Callan, and G. Neubig, “PAL: program-aided language models,” in International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA , ser. Proceedings of Machine Learning Research, A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, and J. Scarlett, Eds., vol. 202. PMLR, 2023, pp. 10 764–10 799. [Online]. Available: https://proceedings.mlr.press/v202/gao23f.html
2023
Later among the works it cites.
2023
Later among the works it cites.
2024
Closest in time.
2024
Closest in time.
X. Du, M. Liu, K. Wang, H. Wang, J. Liu, Y. Chen, J. Feng, C. Sha, X. Peng, and Y. Lou, “Evaluating large language models in class-level code generation,” in Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, ICSE 2024, Lisbon , Portugal, April 14 - 20, 2024 . ACM, 2024, pp. 1496–1508. [Online]. Available: https://doi.org/10.1145/3597503.3639219
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.