Fetching the paper…
Reading the bibliography…
The landscape of software development has witnessed a paradigm shift with the advent of AI-powered assistants, exemplified by GitHub Copilot.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Glue: A multi-task benchmark and analysis platform for natural language understanding, 2019
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R · 2019
Earlier work this paper cites.
Gpt-3: Its nature, scope, limits, and consequences
Floridi, L., and Chiriatti, M · 2020
Earlier work this paper cites.
Codexglue: A machine learning benchmark dataset for code understanding and generation, 2021
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., and Liu, S · 2021
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
Earlier work this paper cites.
Scaling language models: Methods, analysis & insights from training gopher, 2022
Rae, J. W., Borgeaud, S., Cai, T., Millican, K., and Others · 2022
Earlier work this paper cites.
Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model, 2022
Smith, S., Patwary, M., Norick, B., LeGresley, P., Rajbhandari, S., Casper, J., Liu, Z., Prabhumoye, S., Zerveas, G., Korthikanti, V., Zhang, E., Child, R., Aminabadi, R. Y., Bernauer, J., Song, X., Shoeybi, M., He, Y., Houston, M., Tiwary, S., and Catanzaro, B · 2022
Earlier work this paper cites.
Vscuda: Llm based cuda extension for visual studio code
Chen, B., Mustakin, N., Hoang, A., Fuad, S., and Wong, D · 2023
Cited alongside, same era.
In-ide generation-based information support with a large language model, 2023
Nam, D., Macvean, A., Hellendoorn, V., Vasilescu, B., and Myers, B · 2023
Cited alongside, same era.
Gpt 3.5 models, 2023
OpenAI · 2023
Cited alongside, same era.
Gpt-4 technical report, 2023
OpenAI · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Reflexion: Language agents with verbal reinforcement learning, 2023
Shinn, N., Cassano, F., Berman, E., Gopinath, A., Narasimhan, K., and Yao, S · 2023
Autogen: Enabling next-gen llm applications via multi-agent conversation, 2023
Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., Liu, J., Awadallah, A. H., White, R. W., Burger, D., and Wang, C · 2023
Later among the works it cites.
Language agent tree search unifies reasoning acting and planning in language models, 2023
Zhou, A., Yan, K., Shlapentokh-Rothman, M., Wang, H., and Wang, Y.-X · 2023
Later among the works it cites.
https://paperswithcode.com/sota/code-generation-on-humaneval , 2024
Code generation on humaneval - state-of-the-art · 2024
Closest in time.
https://github.com/features/copilot , 2024
Github copilot: Your ai pair programmer · 2024
Closest in time.
Copilot evaluation harness: Evaluating llm-guided software programming, 2024
Agarwal, A., Chan, A., Chandel, S., Jang, J., Miller, S., Moghaddam, R. Z., Mohylevskyy, Y., Sundaresan, N., and Tufano, M · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models, 2023
Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., et al · 2023
Cited alongside, same era.
Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H. P., et al
Cited in the paper.
Gpt-4 technical report, 2024
OpenAI, :, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., Avila, R., Babuschkin, I., Balaji, S., Balcom, V., Baltescu, P., Bao, H., Bavarian, M., Belgum, J., Bello, I., Berdine, J., Bernadett-Shapiro, G., Berner, C., Bogdonoff, L., Boiko, O., Boyd, M., Brakman, A.-L., Brockman, G., Brooks, T., Brundage, M., Button, K., Cai, T., Campbell, R., Cann, A., Carey, B., Carlson, C., Carmichael, R., Chan, B., Chang, C., Chantzis, F., Chen, D., Chen, S., Chen, R., Chen, J., Chen, M., Chess, B., Cho, C., Chu, C., Chung, H. W., Cummings, D., Currier, J., Dai, Y., Decareaux, C., Degry, T., Deutsch, N., Deville, D., Dhar, A., Dohan, D., Dowling, S., Dunning, S., Ecoffet, A., Eleti, A., Eloundou, T., Farhi, D., Fedus, L., Felix, N., Fishman, S. P., Forte, J., Fulford, I., Gao, L., Georges, E., Gibson, C., Goel, V., Gogineni, T., Goh, G., Gontijo-Lopes, R., Gordon, J., Grafstein, M., Gray, S., Greene, R., Gross, J., Gu, S. S., Guo, Y., Hallacy, C., Han, J., Harris, J., He, Y., Heaton, M., Heidecke, J., Hesse, C., Hickey, A., Hickey, W., Hoeschele, P., Houghton, B., Hsu, K., Hu, S., Hu, X., Huizinga, J., Jain, S., Jain, S., Jang, J., Jiang, A., Jiang, R., Jin, H., Jin, D., Jomoto, S., Jonn, B., Jun, H., Kaftan, T., Łukasz Kaiser, Kamali, A., Kanitscheider, I., Keskar, N. S., Khan, T., Kilpatrick, L., Kim, J. W., Kim, C., Kim, Y., Kirchner, J. H., Kiros, J., Knight, M., Kokotajlo, D., Łukasz Kondraciuk, Kondrich, A., Konstantinidis, A., Kosic, K., Krueger, G., Kuo, V., Lampe, M., Lan, I., Lee, T., Leike, J., Leung, J., Levy, D., Li, C. M., Lim, R., Lin, M., Lin, S., Litwin, M., Lopez, T., Lowe, R., Lue, P., Makanju, A., Malfacini, K., Manning, S., Markov, T., Markovski, Y., Martin, B., Mayer, K., Mayne, A., McGrew, B., McKinney, S. M., McLeavey, C., McMillan, P., McNeil, J., Medina, D., Mehta, A., Menick, J., Metz, L., Mishchenko, A., Mishkin, P., Monaco, V., Morikawa, E., Mossing, D., Mu, T., Murati, M., Murk, O., Mély, D., Nair, A., Nakano, R., Nayak, R., Neelakantan, A., Ngo, R., Noh, H., Ouyang, L., O’Keefe, C., Pachocki, J., Paino, A., Palermo, J., Pantuliano, A., Parascandolo, G., Parish, J., Parparita, E., Passos, A., Pavlov, M., Peng, A., Perelman, A., de Avila Belbute Peres, F., Petrov, M., de Oliveira Pinto, H. P., Michael, Pokorny, Pokrass, M., Pong, V. H., Powell, T., Power, A., Power, B., Proehl, E., Puri, R., Radford, A., Rae, J., Ramesh, A., Raymond, C., Real, F., Rimbach, K., Ross, C., Rotsted, B., Roussez, H., Ryder, N., Saltarelli, M., Sanders, T., Santurkar, S., Sastry, G., Schmidt, H., Schnurr, D., Schulman, J., Selsam, D., Sheppard, K., Sherbakov, T., Shieh, J., Shoker, S., Shyam, P., Sidor, S., Sigler, E., Simens, M., Sitkin, J., Slama, K., Sohl, I., Sokolowsky, B., Song, Y., Staudacher, N., Such, F. P., Summers, N., Sutskever, I., Tang, J., Tezak, N., Thompson, M. B., Tillet, P., Tootoonchian, A., Tseng, E., Tuggle, P., Turley, N., Tworek, J., Uribe, J. F. C., Vallone, A., Vijayvergiya, A., Voss, C., Wainwright, C., Wang, J. J., Wang, A., Wang, B., Ward, J., Wei, J., Weinmann, C., Welihinda, A., Welinder, P., Weng, J., Weng, L., Wiethoff, M., Willner, D., Winter, C., Wolrich, S., Wong, H., Workman, L., Wu, S., Wu, J., Wu, M., Xiao, K., Xu, T., Yoo, S., Yu, K., Yuan, Q., Zaremba, W., Zellers, R., Zhang, C., Zhang, M., Zhao, S., Zheng, T., Zhuang, J., Zhuk, W., and Zoph, B
Cited in the paper.
Ding, Y., Wang, Z., Ahmad, W., Ding, H., Tan, M., Jain, N., Ramanathan, M. K., Nallapati, R., Bhatia, P., Roth, D., et al · 2024
Closest in time.