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The capabilities of Large Language Models (LLMs) have significantly evolved, extending from natural language processing to complex tasks like code understanding and generation.
Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei · 2020
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Evaluating large language models trained on code
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. d. O. Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, et al · 2021
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Large language models are zero-shot reasoners
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa · 2022
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Training language models to follow instructions with human feedback
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Gray, et al · 2022
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Chain-of-thought prompting elicits reasoning in large language models
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou, et al · 2022
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Qwen-vl: A frontier large vision-language model with versatile abilities
J. Bai, S. Bai, S. Yang, S. Wang, S. Tan, P. Wang, J. Lin, C. Zhou, and J. Zhou · 2023
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Y. Bang, S. Cahyawijaya, N. Lee, W. Dai, D. Su, B. Wilie, H. Lovenia, Z. Ji, T. Yu, W. Chung, et al · 2023
Cited alongside, same era.
What makes good in-context demonstrations for code intelligence tasks with llms?
S. Gao, X.-C. Wen, C. Gao, W. Wang, H. Zhang, and M. R. Lyu · 2023
Cited alongside, same era.
OpenAI · 2023
Cited alongside, same era.
D. Guo, Q. Zhu, D. Yang, Z. Xie, K. Dong, W. Zhang, G. Chen, X. Bi, Y. Wu, Y. K. Li, F. Luo, Y. Xiong, and W. Liang · 2024
Cited alongside, same era.
A survey on large language models for code generation, 2024
J. Jiang, F. Wang, J. Shen, S. Kim, and S. Kim · 2024
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Position: LLMs can’t plan, but can help planning in LLM-modulo frameworks
S. Kambhampati, K. Valmeekam, L. Guan, M. Verma, K. Stechly, S. Bhambri, L. P. Saldyt, and A. B. Murthy · 2024
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A systematic evaluation of large code models in api suggestion: When, which, and how, 2024
C. Wang, S. Gao, C. Gao, W. Wang, C. Y. Chong, S. Gao, and M. R. Lyu · 2024
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Tree of thoughts: Deliberate problem solving with large language models
S. Yao, D. Yu, J. Zhao, I. Shafran, T. Griffiths, Y. Cao, and K. Narasimhan · 2024
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Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions
T. Y. Zhuo, M. C. Vu, J. Chim, H. Hu, W. Yu, R. Widyasari, I. N. B. Yusuf, H. Zhan, J. He, I. Paul, et al · 2024
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B. Hui, J. Yang, Z. Cui, J. Yang, D. Liu, L. Zhang, T. Liu, J. Zhang, B. Yu, K. Dang, A. Yang, R. Men, F. Huang, X. Ren, X. Ren, J. Zhou, and J. Lin · 2024
Cited alongside, same era.
Document-level machine translation with large language models
L. Wang, C. Lyu, T. Ji, Z. Zhang, D. Yu, S. Shi, and Z. Tu
Cited in the paper.
Codet5+: Open code large language models for code understanding and generation, 2023b
Y. Wang, H. Le, A. D. Gotmare, N. D. Q. Bui, J. Li, and S. C. H. Hoi
Cited in the paper.
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