Unraveling the Potential of Large Language Models in Code Translation: How Far Are We?

A. T. Nguyen, T. T. Nguyen, and T. N. Nguyen, “Divide-and-Conquer approach for multi-phase statistical migration for source code (T),” in ASE , 2015, pp. 585–596

2015

Earlier work this paper cites.

X. Chen, C. Liu, and D. Song, “Tree-to-tree neural networks for program translation,” in NeurIPS , 2018, pp. 2552–2562

2018

Earlier work this paper cites.

B. Rozière, M. Lachaux, L. Chanussot, and G. Lample, “Unsupervised translation of programming languages,” in NeurIPS , 2020

2020

Earlier work this paper cites.

S. Lu, D. Guo, S. Ren, and et al., “CodeXGLUE: A machine learning benchmark dataset for code understanding and generation,” in NeurIPS Datasets and Benchmarks , 2021

2021

Earlier work this paper cites.

M. Chen, J. Tworek, H. Jun, Q. Yuan, and et al., “Evaluating large language models trained on code,” CoRR , vol. abs/2107.03374, 2021

Original

2021

Earlier work this paper cites.

D. Kocetkov, R. Li, L. B. Allal, J. Li, C. Mou, C. M. Ferrandis, Y. Jernite, M. Mitchell, S. Hughes, T. Wolf, D. Bahdanau, L. von Werra, and H. de Vries, “The Stack: 3 TB of permissively licensed source code,” CoRR , vol. abs/2211.15533, 2022

Original

2022

Earlier work this paper cites.

T. Dettmers, M. Lewis, Y. Belkada, and L. Zettlemoyer, “GPT3.int8(): 8-bit matrix multiplication for transformers at scale,” in NeurIPS , 2022

2022

Earlier work this paper cites.

J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. H. Chi, Q. V. Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” in NeurIPS , 2022

2022

Earlier work this paper cites.

B. Rozière, J. Zhang, F. Charton, M. Harman, G. Synnaeve, and G. Lample, “Leveraging automated unit tests for unsupervised code translation,” in ICLR , 2022

2022

Earlier work this paper cites.

M. Jiao, T. Yu, X. Li, G. Qiu, X. Gu, and B. Shen, “On the evaluation of neural code translation: Taxonomy and benchmark,” in ASE , 2023, pp. 1529–1541

2023

Earlier work this paper cites.

R. Li, L. B. Allal, Y. Zi, and et al., “StarCoder: may the source be with you!” CoRR , vol. abs/2305.06161, 2023

Original

2023

Cited alongside, same era.

S. Chaudhary, “Code Alpaca: An instruction-following LLaMA model for code generation,” https://github.com/sahil280114/codealpaca , 2023

2023

Cited alongside, same era.

N. Muennighoff, Q. Liu, A. Zebaze, Q. Zheng, B. Hui, T. Y. Zhuo, S. Singh, X. Tang, L. von Werra, and S. Longpre, “Octopack: Instruction tuning code large language models,” CoRR , vol. abs/2308.07124, 2023

Original

2023

Cited alongside, same era.

B. Rozière, J. Gehring, F. Gloeckle, S. Sootla, and et al., “Code Llama: Open foundation models for code,” CoRR , vol. abs/2308.12950, 2023

Original

2023

Cited alongside, same era.

H. Touvron, L. Martin, K. Stone, P. Albert, and et al., “Llama 2: Open foundation and fine-tuned chat models,” CoRR , vol. abs/2307.09288, 2023

G. Penedo, Q. Malartic, D. Hesslow, R. Cojocaru, H. Alobeidli, A. Cappelli, B. Pannier, E. Almazrouei, and J. Launay, “The RefinedWeb dataset for falcon LLM: outperforming curated corpora with web data only,” in NeurIPS , 2023

2023

Later among the works it cites.

W. U. Ahmad, M. G. R. Tushar, S. Chakraborty, and K. Chang, “AVATAR: A parallel corpus for Java-Python program translation,” in Findings of ACL , 2023, pp. 2268–2281

2023

Later among the works it cites.

Y. Fu, H. Peng, A. Sabharwal, P. Clark, and T. Khot, “Complexity-based prompting for multi-step reasoning,” in ICLR , 2023

2023

Later among the works it cites.

M. Szafraniec, B. Rozière, H. Leather, P. Labatut, F. Charton, and G. Synnaeve, “Code translation with compiler representations,” in ICLR , 2023

2023

Later among the works it cites.

A. Josh, A. Steven, A. Sandhini et al. , “GPT-4 technical report,” CoRR , vol. abs/2303.08774, 2023