Fetching the paper…
Reading the bibliography…
Large language models (LLMs) have shown remarkable progress in code generation, but their generated code often suffers from inefficiency, resulting in longer execution times and higher memory consumption.
Reflective learning: Key to learning from experience
Boyd, E. M. and Fales, A. W · 1983
Earlier work this paper cites.
Is software “green”? application development environments and energy efficiency in open source applications
Capra, E., Francalanci, C., and Slaughter, S. A · 2012
Earlier work this paper cites.
Learning from errors
Metcalfe, J · 2017
Earlier work this paper cites.
Can neural machine translation be improved with user feedback?
Kreutzer, J., Khadivi, S., Matusov, E., and Riezler, S · 2018
Earlier work this paper cites.
Language models are few-shot learners
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., and Amodei, D · 2020
Earlier work this paper cites.
Unsupervised translation of programming languages
Rozière, B., Lachaux, M., Chanussot, L., and Lample, G · 2020
Earlier work this paper cites.
Program synthesis with large language models
Austin, J., Odena, A., Nye, M. I., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C. J., Terry, M., Le, Q. V., and Sutton, C · 2021
Earlier work this paper cites.
Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H. P., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Such, F. P., Cummings, D., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W. H., Nichol, A., Paino, A., Tezak, N., Tang, J., Babuschkin, I., Balaji, S., Jain, S., Saunders, W., Hesse, C., Carr, A. N., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., and Zaremba, W · 2021
Earlier work this paper cites.
Codesc: A large code-description parallel dataset
Hasan, M., Muttaqueen, T., Ishtiaq, A. A., Mehrab, K. S., Haque, M. M. A., Hasan, T., Ahmad, W. U., Iqbal, A., and Shahriyar, R · 2021
Earlier work this paper cites.
A process for analysing the energy efficiency of software
Mancebo, J., Garcia, F., and Calero, C · 2021
Earlier work this paper cites.
Ranking programming languages by energy efficiency
Pereira, R., Couto, M., Ribeiro, F., Rua, R., Cunha, J., Fernandes, J. P., and Saraiva, J · 2021
Earlier work this paper cites.
Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Wang, Y., Wang, W., Joty, S. R., and Hoi, S. C. H · 2021
Earlier work this paper cites.
Few-shot training llms for project-specific code-summarization
Ahmed, T. and Devanbu, P. T · 2022
Earlier work this paper cites.
Improving alignment of dialogue agents via targeted human judgements
Glaese, A., McAleese, N., Trebacz, M., Aslanides, J., Firoiu, V., Ewalds, T., Rauh, M., Weidinger, L., Chadwick, M. J., Thacker, P., Campbell-Gillingham, L., Uesato, J., Huang, P., Comanescu, R., Yang, F., See, A., Dathathri, S., Greig, R., Chen, C., Fritz, D., Elias, J. S., Green, R., Mokrá, S., Fernando, N., Wu, B., Foley, R., Young, S., Gabriel, I., Isaac, W., Mellor, J., Hassabis, D., Kavukcuoglu, K., Hendricks, L. A., and Irving, G · 2022
Earlier work this paper cites.
Fixeval: Execution-based evaluation of program fixes for competitive programming problems
Haque, M. M. A., Ahmad, W. U., Lourentzou, I., and Brown, C · 2022
Earlier work this paper cites.
Competition-level code generation with alphacode
Li, Y., Choi, D. H., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Lago, A. D., Hubert, T., Choy, P., de Masson d’Autume, C., Babuschkin, I., Chen, X., Huang, P., Welbl, J., Gowal, S., Cherepanov, A., Molloy, J., Mankowitz, D. J., Robson, E. S., Kohli, P., de Freitas, N., Kavukcuoglu, K., and Vinyals, O · 2022
Earlier work this paper cites.
Type4py: Practical deep similarity learning-based type inference for python
Mir, A. M., Latoskinas, E., Proksch, S., and Gousios, G · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P. F., Leike, J., and Lowe, R · 2022
Earlier work this paper cites.
Finetuned language models are zero-shot learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 2022
Earlier work this paper cites.
AVATAR: A parallel corpus for java-python program translation
Ahmad, W. U., Tushar, M. G. R., Chakraborty, S., and Chang, K · 2023
Earlier work this paper cites.
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., Umapathi, L. K., Anderson, C. J., Zi, Y., Lamy-Poirier, J., Schoelkopf, H., Troshin, S., Abulkhanov, D., Romero, M., Lappert, M., Toni, F. D., del Río, B. G., Liu, Q., Bose, S., Bhattacharyya, U., Zhuo, T. Y., Yu, I., Villegas, P., Zocca, M., Mangrulkar, S., Lansky, D., Nguyen, H., Contractor, D., Villa, L., Li, J., Bahdanau, D., Jernite, Y., Hughes, S., Fried, D., Guha, A., de Vries, H., and von Werra, L · 2023
Earlier work this paper cites.
Gemini: A family of highly capable multimodal models
Anil, R., Borgeaud, S., Wu, Y., Alayrac, J., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., Silver, D., Petrov, S., Johnson, M., Antonoglou, I., Schrittwieser, J., Glaese, A., Chen, J., Pitler, E., Lillicrap, T. P., Lazaridou, A., Firat, O., Molloy, J., Isard, M., Barham, P. R., Hennigan, T., Lee, B., Viola, F., Reynolds, M., Xu, Y., Doherty, R., Collins, E., Meyer, C., Rutherford, E., Moreira, E., Ayoub, K., Goel, M., Tucker, G., Piqueras, E., Krikun, M., Barr, I., Savinov, N., Danihelka, I., Roelofs, B., White, A., Andreassen, A., von Glehn, T., Yagati, L., Kazemi, M., Gonzalez, L., Khalman, M., Sygnowski, J., and et al · 2023
Earlier work this paper cites.
Code alpaca: An instruction-following llama model for code generation
Chaudhary, S · 2023
Cited alongside, same era.
Teaching large language models to self-debug
Chen, X., Lin, M., Schärli, N., and Zhou, D · 2023
Cited alongside, same era.
Deepseek coder: Let the code write itself, 2023
DeepSeekAI · 2023
Cited alongside, same era.
Large language models are edge-case fuzzers: Testing deep learning libraries via fuzzgpt
Deng, Y., Xia, C. S., Yang, C., Zhang, S. D., Yang, S., and Zhang, L · 2023
Cited alongside, same era.
Large language models for software engineering: Survey and open problems
Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., and Zhang, J. M · 2023
Cited alongside, same era.
Self-edit: Fault-aware code editor for code generation
Zhang, K., Li, Z., Li, J., Li, G., and Jin, Z · 2023
Later among the works it cites.
ALGO: synthesizing algorithmic programs with generated oracle verifiers
Zhang, K., Wang, D., Xia, J., Wang, W. Y., and Li, L · 2023
Later among the works it cites.
Introducing the next generation of claude, 2024
Anthropic · 2024
Closest in time.
MHPP: exploring the capabilities and limitations of language models beyond basic code generation
Dai, J., Lu, J., Feng, Y., Ruan, R., Cheng, M., Tan, H., and Guo, Z · 2024
Closest in time.
Mercury: An efficiency benchmark for llm code synthesis
Du, M., Luu, A. T., Ji, B., and Ng, S.-K · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Incoder: A generative model for code infilling and synthesis
Fried, D., Aghajanyan, A., Lin, J., Wang, S., Wallace, E., Shi, F., Zhong, R., Yih, S., Zettlemoyer, L., and Lewis, M · 2023
Cited alongside, same era.
RARR: researching and revising what language models say, using language models
Gao, L., Dai, Z., Pasupat, P., Chen, A., Chaganty, A. T., Fan, Y., Zhao, V. Y., Lao, N., Lee, H., Juan, D., and Guu, K · 2023
Cited alongside, same era.
CRITIC: large language models can self-correct with tool-interactive critiquing
Gou, Z., Shao, Z., Gong, Y., Shen, Y., Yang, Y., Duan, N., and Chen, W · 2023
Cited alongside, same era.
Gunasekar, S., Zhang, Y., Aneja, J., Mendes, C. C. T., Giorno, A. D., Gopi, S., Javaheripi, M., Kauffmann, P., de Rosa, G., Saarikivi, O., Salim, A., Shah, S., Behl, H. S., Wang, X., Bubeck, S., Eldan, R., Kalai, A. T., Lee, Y. T., and Li, Y · 2023
Cited alongside, same era.
Impact of code language models on automated program repair
Jiang, N., Liu, K., Lutellier, T., and Tan, L · 2023
Cited alongside, same era.
Codamosa: Escaping coverage plateaus in test generation with pre-trained large language models
Lemieux, C., Inala, J. P., Lahiri, S. K., and Sen, S · 2023
Cited alongside, same era.
Wizardcoder: Empowering code large language models with evol-instruct
Luo, Z., Xu, C., Zhao, P., Sun, Q., Geng, X., Hu, W., Tao, C., Ma, J., Lin, Q., and Jiang, D · 2023
Cited alongside, same era.
Guo, D., Zhu, Q., Yang, D., Xie, Z., Dong, K., Zhang, W., Chen, G., Bi, X., Wu, Y., Li, Y., et al · 2024
Closest in time.
Effibench: Benchmarking the efficiency of automatically generated code
Huang, D., Zhang, J. M., Qing, Y., and Cui, H · 2024
Closest in time.
Wizardcoder: Empowering code large language models with evol-instruct
Luo, Z., Xu, C., Zhao, P., Sun, Q., Geng, X., Hu, W., Tao, C., Ma, J., Lin, Q., and Jiang, D · 2024
Closest in time.
Introducing meta llama 3: The most capable openly available llm to date, 2024
Meta · 2024
Closest in time.
The world’s most widely adopted ai developer tool., 2024
Microsoft · 2024
Closest in time.
Octopack: Instruction tuning code large language models
Muennighoff, N., Liu, Q., Zebaze, A. R., Zheng, Q., Hui, B., Zhuo, T. Y., Singh, S., Tang, X., von Werra, L., and Longpre, S · 2024
Closest in time.
On evaluating the efficiency of source code generated by llms
Niu, C., Zhang, T., Li, C., Luo, B., and Ng, V · 2024
Closest in time.
How efficient is llm-generated code? a rigorous & high-standard benchmark
Qiu, R., Zeng, W. W., Tong, H., Ezick, J., and Lott, C · 2024
Closest in time.
Efficient and green large language models for software engineering: Vision and the road ahead
Shi, J., Yang, Z., and Lo, D · 2024
Closest in time.
Learning Performance-Improving Code Edits
Shypula, A., Madaan, A., Zeng, Y., Alon, U., Gardner, J., Hashemi, M., Neubig, G., Ranganathan, P., Bastani, O., and Yazdanbakhsh, A · 2024
Closest in time.
Ecco: Can we improve model-generated code efficiency without sacrificing functional correctness?
Waghjale, S., Veerendranath, V., Wang, Z. Z., and Fried, D · 2024
Closest in time.
Magicoder: Empowering code generation with oss-instruct
Wei, Y., Wang, Z., Liu, J., Ding, Y., and Zhang, L · 2024
Closest in time.
Wizardlm: Empowering large pre-trained language models to follow complex instructions
Xu, C., Sun, Q., Zheng, K., Geng, X., Zhao, P., Feng, J., Tao, C., Lin, Q., and Jiang, D · 2024
Closest in time.
Learning from correctness without prompting makes LLM efficient reasoner
Yao, Y., Wu, H., Guo, Z., Zhou, B., Gao, J., Luo, S., Hou, H., Fu, X., and Song, L · 2024
Closest in time.
Self-guide: Better task-specific instruction following via self-synthetic finetuning
Zhao, C., Jia, X., Viswanathan, V., Wu, T., and Neubig, G · 2024
Closest in time.
Opencodeinterpreter: Integrating code generation with execution and refinement
Zheng, T., Zhang, G., Shen, T., Liu, X., Lin, B. Y., Fu, J., Chen, W., and Yue, X · 2024
Closest in time.