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Recent advances in retrieval-augmented generation (RAG) have initiated a new era in repository-level code completion.
The distribution of the flora in the alpine zone.1
Jaccard, P · 1912
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Binary codes capable of correcting deletions, insertions, and reversals
Levenshtein, V. I. et al · 1966
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On the criteria to be used in decomposing systems into modules
Parnas, D. L · 1972
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Supporting reuse by delivering task-relevant and personalized information
Ye, Y. and Fischer, G · 2002
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Automatic method completion
Hill, R. and Rideout, J · 2004
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Codebleu: a method for automatic evaluation of code synthesis
Ren, S., Guo, D., Lu, S., Zhou, L., Liu, S., Tang, D., Sundaresan, N., Zhou, M., Blanco, A., and Ma, S · 2009
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On the localness of software
Tu, Z., Su, Z., and Devanbu, P. T · 2014
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Are deep neural networks the best choice for modeling source code?
Hellendoorn, V. J. and Devanbu, P. T · 2017
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Intellicode compose: code generation using transformer
Svyatkovskiy, A., Deng, S. K., Fu, S., and Sundaresan, N · 2020
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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
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Efficient nearest neighbor language models
He, J., Neubig, G., and Berg-Kirkpatrick, T · 2021
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Cocosum: Contextual code summarization with multi-relational graph neural network
Wang, Y., Shi, E., Du, L., Yang, X., Hu, Y., Han, S., Zhang, H., and Zhang, D · 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., and Chen, M · 2022
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You can’t pick your neighbors, or can you? when and how to rely on retrieval in the kNN-LM
Drozdov, A., Wang, S., Rahimi, R., McCallum, A., Zamani, H., and Iyyer, M · 2022
Cited alongside, same era.
UniXcoder: Unified cross-modal pre-training for code representation
Guo, D., Lu, S., Duan, N., Wang, Y., Zhou, M., and Yin, J · 2022
Cited alongside, same era.
Language models (mostly) know what they know
Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., Schiefer, N., Hatfield-Dodds, Z., DasSarma, N., Tran-Johnson, E., et al · 2022
Cited alongside, same era.
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
Cited alongside, same era.
ReACC: A retrieval-augmented code completion framework
Lu, S., Duan, N., Han, H., Guo, D., Hwang, S.-w., and Svyatkovskiy, A · 2022
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
Later among the works it cites.
OpenAI · 2023
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Better context makes better code language models: A case study on function call argument completion
Pei, H., Zhao, J., Lausen, L., Zha, S., and Karypis, G · 2023
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In-context retrieval-augmented language models
Ram, O., Levine, Y., Dalmedigos, I., Muhlgay, D., Shashua, A., Leyton-Brown, K., and Shoham, Y · 2023
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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
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Cited alongside, same era.
When language model meets private library
Zan, D., Chen, B., Lin, Z., Guan, B., Yongji, W., and Lou, J.-G · 2022
Cited alongside, same era.
Accelerating large language model decoding with speculative sampling
Chen, C., Borgeaud, S., Irving, G., Lespiau, J.-B., Sifre, L., and Jumper, J · 2023
Cited alongside, same era.
Crosscodeeval: A diverse and multilingual benchmark for cross-file code completion
Ding, Y., Wang, Z., Ahmad, W. U., Ding, H., Tan, M., Jain, N., Ramanathan, M. K., Nallapati, R., Bhatia, P., Roth, D., and Xiang, B · 2023
Cited alongside, same era.
ContraCLM: Contrastive learning for causal language model
Jain, N., Zhang, D., Ahmad, W. U., Wang, Z., Nan, F., Li, X., Tan, M., Nallapati, R., Ray, B., Bhatia, P., Ma, X., and Xiang, B · 2023
Cited alongside, same era.
Active retrieval augmented generation
Jiang, Z., Xu, F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., and Neubig, G · 2023
Cited alongside, same era.
Efficient memory management for large language model serving with pagedattention
Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J., Zhang, H., and Stoica, I · 2023
Cited alongside, same era.
The web can be your oyster for improving language models
Li, J., Tang, T., Zhao, W. X., Wang, J., Nie, J.-Y., and Wen, J.-R · 2023
Cited alongside, same era.
Replug: Retrieval-augmented black-box language models
Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Zettlemoyer, L., and Yih, W.-t · 2023
Later among the works it cites.
Repository-level prompt generation for large language models of code
Shrivastava, D., Larochelle, H., and Tarlow, D · 2023
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Self-knowledge guided retrieval augmentation for large language models
Wang, Y., Li, P., Sun, M., and Liu, Y · 2023
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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
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Docprompting: Generating code by retrieving the docs
Zhou, S., Alon, U., Xu, F. F., Jiang, Z., and Neubig, G · 2023
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Self-RAG: Learning to retrieve, generate, and critique through self-reflection
Asai, A., Wu, Z., Wang, Y., Sil, A., and Hajishirzi, H · 2024
Closest in time.
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., and Xiang, B · 2024
Closest in time.