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With the success of large language models (LLMs) of code and their use as code assistants (e.g.
A probabilistic model of information retrieval: development and comparative experiments - part 1
Jones, K. S., Walker, S., and Robertson, S. E · 2000
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Earlier work this paper cites.
Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Are deep neural networks the best choice for modeling source code?
Hellendoorn, V. J. and Devanbu, P · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, u., and Polosukhin, I · 2017
Earlier work this paper cites.
The adverse effects of code duplication in machine learning models of code
Allamanis, M · 2019
Earlier work this paper cites.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., 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., 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., and Amodei, D · 2020
Earlier work this paper cites.
Codebert: A pre-trained model for programming and natural languages
Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., et al · 2020
Earlier work this paper cites.
Graphcodebert: Pre-training code representations with data flow
Guo, D., Ren, S., Lu, S., Feng, Z., Tang, D., Liu, S., Zhou, L., Duan, N., Svyatkovskiy, A., Fu, S., et al · 2020
Earlier work this paper cites.
Learning and evaluating contextual embedding of source code
Kanade, A., Maniatis, P., Balakrishnan, G., and Shi, K · 2020
Earlier work this paper cites.
Generalization through memorization: Nearest neighbor language models
Khandelwal, U., Levy, O., Jurafsky, D., Zettlemoyer, L., and Lewis, M · 2020
Earlier work this paper cites.
AutoPrompt: Eliciting knowledge from language models with automatically generated prompts
Shin, T., Razeghi, Y., IV, R. L. L., Wallace, E., and Singh, S · 2020
Earlier work this paper cites.
On-the-fly adaptation of source code models
Shrivastava, D., Larochelle, H., and Tarlow, D · 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
Earlier work this paper cites.
FLEX: Unifying evaluation for few-shot NLP
Bragg, J., Cohan, A., Lo, K., and Beltagy, I · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Making pre-trained language models better few-shot learners
Gao, T., Fisch, A., and Chen, D · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L. and Liang, P · 2021
Cited alongside, same era.
Discovering the syntax and strategies of natural language programming with generative language models
Jiang, E., Toh, E., Molina, A., Olson, K., Kayacik, C., Donsbach, A., Cai, C. J., and Terry, M · 2022
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Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
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Competition-level code generation with alphacode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al · 2022
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What makes good in-context examples for GPT-3?
Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., and Chen, W · 2022
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Do users write more insecure code with ai assistants?
Perry, N., Srivastava, M., Kumar, D., and Boneh, D · 2022
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Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z., and Tang, J · 2021
Cited alongside, same era.
Embedding api dependency graph for neural code generation
Lyu, C., Wang, R., Zhang, H., Zhang, H., and Hu, S · 2021
Cited alongside, same era.
Learning how to ask: Querying LMs with mixtures of soft prompts
Qin, G. and Eisner, J · 2021
Cited alongside, same era.
Prompt programming for large language models: Beyond the few-shot paradigm
Reynolds, L. and McDonell, K · 2021
Cited alongside, same era.
Exploiting cloze-questions for few-shot text classification and natural language inference
Schick, T. and Schütze, H · 2021
Cited alongside, same era.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Wang, B. and Komatsuzaki, A · 2021
Cited alongside, same era.
Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Wang, Y., Wang, W., Joty, S., and Hoi, S. C · 2021
Cited alongside, same era.
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
Closest in time.
Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S. G., Novikov, A., Barth-Maron, G., Gimenez, M., Sulsky, Y., Kay, J., Springenberg, J. T., et al · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Investigating explainability of generative ai for code through scenario-based design
Sun, J., Liao, Q. V., Muller, M., Agarwal, M., Houde, S., Talamadupula, K., and Weisz, J. D · 2022
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No more fine-tuning? an experimental evaluation of prompt tuning in code intelligence
Wang, C., Yang, Y., Gao, C., Peng, Y., Zhang, H., and Lyu, M. R · 2022
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Chain of thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., and Zhou, D · 2022
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Memorizing transformers
Wu, Y., Rabe, M. N., Hutchins, D., and Szegedy, C · 2022
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PRIMERA: Pyramid-based masked sentence pre-training for multi-document summarization
Xiao, W., Beltagy, I., Carenini, G., and Cohan, A · 2022
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., and Neubig, G · 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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