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Code completion models have made significant progress in recent years.
Codesearchnet challenge: Evaluating the state of semantic code search
Husain, H., Wu, H.H., Gazit, T., Allamanis, M., Brockschmidt, M., 2019 · 1909
Earlier work this paper cites.
The distribution of the flora in the alpine zone. 1
Jaccard, P., 1912 · 1912
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The probabilistic relevance framework: Bm25 and beyond
Robertson, S., Zaragoza, H., et al., 2009 · 2009
Earlier work this paper cites.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., 2019 · 2019
Earlier work this paper cites.
Language models are few-shot learners, in: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (Eds.), Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D., 2020 · 2020
Earlier work this paper cites.
Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., Steinhardt, J., 2020 · 2020
Earlier work this paper cites.
Program synthesis with large language models
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., et al., 2021 · 2021
Earlier work this paper cites.
GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow
Black, S., Gao, L., Wang, P., Leahy, C., Biderman, S., 2021 · 2021
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Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al., 2021 · 2021
Earlier work this paper cites.
Cosqa: 20,000+ web queries for code search and question answering
Huang, J., Tang, D., Shou, L., Gong, M., Xu, K., Jiang, D., Zhou, M., Duan, N., 2021 · 2021
Earlier work this paper cites.
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)
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
Earlier work this paper cites.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Wang, B., Komatsuzaki, A., 2021 · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
Review4repair: Code review aided automatic program repairing 143, 106765
Huq, F., Hasan, M., Haque, M.M.A., Mahbub, S., Iqbal, A., Ahmed, T., 2022 · 2022
Earlier work this paper cites.
The stack: 3 tb of permissively licensed source code
Kocetkov, D., Li, R., Allal, L.B., Li, J., Mou, C., Ferrandis, C.M., Jernite, Y., Mitchell, M., Hughes, S., Wolf, T., et al., 2022 · 2022
Cited alongside, same era.
Tssb-3m: Mining single statement bugs at massive scale, in: Proceedings of the 19th International Conference on Mining Software Repositories, pp. 418–422
Richter, C., Wehrheim, H., 2022 · 2022
Cited alongside, same era.
Xlcost: A benchmark dataset for cross-lingual code intelligence
Zhu, M., Jain, A., Suresh, K., Ravindran, R., Tipirneni, S., Reddy, C.K., 2022 · 2022
Cited alongside, same era.
Gpt-4 technical report
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al., 2023 · 2023
Cited alongside, same era.
Guiding language models of code with global context using monitors
Agrawal, L.A., Kanade, A., Goyal, N., Lahiri, S.K., Rajamani, S.K., 2023 · 2023
Runbugrun–an executable dataset for automated program repair
Prenner, J.A., Robbes, R., 2023 · 2023
Later among the works it cites.
Code llama: Open foundation models for code
Roziere, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X.E., Adi, Y., Liu, J., Remez, T., Rapin, J., et al., 2023 · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al., 2023 · 2023
Later among the works it cites.
Codetransocean: A comprehensive multilingual benchmark for code translation
Yan, W., Tian, Y., Li, Y., Chen, Q., Wang, W., 2023 · 2023
Later among the works it cites.
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
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
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Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
Codeplan: Repository-level coding using llms and planning
Bairi, R., Sonwane, A., Kanade, A., Iyer, A., Parthasarathy, S., Rajamani, S., Ashok, B., Shet, S., et al., 2023 · 2023
Cited alongside, same era.
Palm: Scaling language modeling with pathways 24, 1–113
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al., 2023 · 2023
Cited alongside, same era.
Crosscodeeval: A diverse and multilingual benchmark for cross-file code completion, in: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (Eds.), Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023
Ding, Y., Wang, Z., Ahmad, W.U., Ding, H., Tan, M., Jain, N., Ramanathan, M.K., Nallapati, R., Bhatia, P., Roth, D., Xiang, B., 2023 · 2023
Cited alongside, same era.
Owl: A large language model for it operations
Guo, H., Yang, J., Liu, J., Yang, L., Chai, L., Bai, J., Peng, J., Hu, X., Chen, C., Zhang, D., et al., 2023 · 2023
Cited alongside, same era.
On the evaluation of neural code translation: Taxonomy and benchmark, in: 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE), IEEE. pp. 1529–1541
Jiao, M., Yu, T., Li, X., Qiu, G., Gu, X., Shen, B., 2023 · 2023
Cited alongside, same era.
Mt-bench-101: A fine-grained benchmark for evaluating large language models in multi-turn dialogues
Bai, G., Liu, J., Bu, X., He, Y., Liu, J., Zhou, Z., Lin, Z., Su, W., Ge, T., Zheng, B., Ouyang, W., 2024 · 2024
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Chinese tiny llm: Pretraining a chinese-centric large language model
Du, X., Yu, Z., Gao, S., Pan, D., Cheng, Y., Ma, Z., Yuan, R., Qu, X., Liu, J., Zheng, T., Luo, X., Zhou, G., Yuan, B., Chen, W., Fu, J., Zhang, G., 2024 · 2024
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Cruxeval: A benchmark for code reasoning, understanding and execution
Gu, A., Rozière, B., Leather, H., Solar-Lezama, A., Synnaeve, G., Wang, S.I., 2024 · 2024
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Procqa: A large-scale community-based programming question answering dataset for code search
Li, Z., Zhang, J., Yin, C., Ouyang, Y., Rong, W., 2024 · 2024
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E2-llm: Efficient and extreme length extension of large language models
Liu, J., Bai, Z., Zhang, Y., Zhang, C., Zhang, Y., Zhang, G., Wang, J., Que, H., Chen, Y., Su, W., et al., 2024 · 2024
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Starcoder 2 and the stack v2: The next generation
Lozhkov, A., Li, R., Allal, L.B., Cassano, F., Lamy-Poirier, J., Tazi, N., Tang, A., Pykhtar, D., Liu, J., Wei, Y., et al., 2024 · 2024
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Repohyper: Better context retrieval is all you need for repository-level code completion
Phan, H.N., Phan, H.N., Nguyen, T.N., Bui, N.D., 2024 · 2024
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Roformer: Enhanced transformer with rotary position embedding 568, 127063
Su, J., Ahmed, M., Lu, Y., Pan, S., Bo, W., Liu, Y., 2024 · 2024
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Unicoder: Scaling code large language model via universal code
Sun, T., Chai, L., Jian Yang, Y.Y., Guo, H., Liu, J., Wang, B., Yang, L., Li, Z., 2024 · 2024
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Debugbench: Evaluating debugging capability of large language models
Tian, R., Ye, Y., Qin, Y., Cong, X., Lin, Y., Liu, Z., Sun, M., 2024 · 2024
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Conceptmath: A bilingual concept-wise benchmark for measuring mathematical reasoning of large language models
Wu, Y., Liu, J., Bu, X., Liu, J., Zhou, Z., Zhang, Y., Zhang, C., Bai, Z., Chen, H., Ge, T., Ouyang, W., Su, W., Zheng, B., 2024 · 2024
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Codereval: A benchmark of pragmatic code generation with generative pre-trained models, in: Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, pp. 1–12
Yu, H., Shen, B., Ran, D., Zhang, J., Zhang, Q., Ma, Y., Liang, G., Li, Y., Wang, Q., Xie, T., 2024 · 2024
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Map-neo: Highly capable and transparent bilingual large language model series
Zhang, G., Qu, S., Liu, J., Zhang, C., Lin, C., Yu, C.L., Pan, D., Cheng, E., Liu, J., Lin, Q., Yuan, R., Zheng, T., Pang, W., Du, X., Liang, Y., Ma, Y., Li, Y., Ma, Z., Lin, B., Benetos, E., Yang, H., Zhou, J., Ma, K., Liu, M., Niu, M., Wang, N., Que, Q., Liu, R., Liu, S., Guo, S., Gao, S., Zhou, W., Zhang, X., Zhou, Y., Wang, Y., Bai, Y., Zhang, Y., Zhang, Y., Wang, Z., Yang, Z., Zhao, Z., Zhang, J., Ouyang, W., Huang, W., Chen, W., 2024 · 2024
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