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Instruction tuning is a supervised fine-tuning approach that significantly improves the ability of large language models (LLMs) to follow human instructions.
Improving language understanding by generative pre-training
A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever · 2018
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Adafactor: Adaptive learning rates with sublinear memory cost, 2018
N. Shazeer and M. Stern · 2018
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Zero: memory optimizations toward training trillion parameter models
S. Rajbhandari, J. Rasley, O. Ruwase, and Y. He · 2020
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Unsupervised translation of programming languages
B. Roziere, M.-A. Lachaux, L. Chanussot, and G. Lample · 2020
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Avatar: A parallel corpus for java-python program translation
W. U. Ahmad, M. G. R. Tushar, S. Chakraborty, and K.-W. Chang · 2021
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Program synthesis with large language models
J. Austin, A. Odena, M. I. Nye, M. Bosma, H. Michalewski, D. Dohan, E. Jiang, C. J. Cai, M. Terry, Q. V. Le, and C. Sutton · 2021
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Evaluating large language models trained on code, 2021
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. de Oliveira Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, A. Ray, R. Puri, G. Krueger, M. Petrov, H. Khlaaf, G. Sastry, P. Mishkin, B. Chan, S. Gray, N. Ryder, M. Pavlov, A. Power, L. Kaiser, M. Bavarian, C. Winter, P. Tillet, F. P. Such, D. Cummings, M. Plappert, F. Chantzis, E. Barnes, A. Herbert-Voss, W. H. Guss, A. Nichol, A. Paino, N. Tezak, J. Tang, I. Babuschkin, S. Balaji, S. Jain, W. Saunders, C. Hesse, A. N. Carr, J. Leike, J. Achiam, V. Misra, E. Morikawa, A. Radford, M. Knight, M. Brundage, M. Murati, K. Mayer, P. Welinder, B. McGrew, D. Amodei, S. McCandlish, I. Sutskever, and W. Zaremba · 2021
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CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Y. Wang, W. Wang, S. Joty, and S. C. Hoi · 2021
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The stack: 3 tb of permissively licensed source code, 2022
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 · 2022
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Ds-1000: A natural and reliable benchmark for data science code generation, 2022
Y. Lai, C. Li, Y. Wang, T. Zhang, R. Zhong, L. Zettlemoyer, S. W. tau Yih, D. Fried, S. Wang, and T. Yu · 2022
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CodeRL: Mastering code generation through pretrained models and deep reinforcement learning
H. Le, Y. Wang, A. D. Gotmare, S. Savarese, and S. Hoi · 2022
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Chatgpt: Optimizing language models for dialogue
OpenAI · 2022
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Training language models to follow instructions with human feedback, 2022
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, and R. Lowe · 2022
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Less training, more repairing please: Revisiting automated program repair via zero-shot learning, 2022
C. S. Xia and L. Zhang · 2022
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Terms of service, 7 2023
Anthropic · 2023
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Code alpaca: An instruction-following llama model for code generation
S. Chaudhary · 2023
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Large language models for compiler optimization
C. Cummins, V. Seeker, D. Grubisic, M. Elhoushi, Y. Liang, B. Roziere, J. Gehring, F. Gloeckle, K. Hazelwood, G. Synnaeve, et al · 2023
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Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models, 2023
Y. Deng, C. S. Xia, H. Peng, C. Yang, and L. Zhang · 2023
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Classeval: A manually-crafted benchmark for evaluating llms on class-level code generation
X. Du, M. Liu, K. Wang, H. Wang, J. Liu, Y. Chen, J. Feng, C. Sha, X. Peng, and Y. Lou · 2023
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Generative ai terms of service, 8 2023
Google · 2023
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Textbooks are all you need, 2023
S. Gunasekar, Y. Zhang, J. Aneja, C. C. T. Mendes, A. D. Giorno, S. Gopi, M. Javaheripi, P. Kauffmann, G. de Rosa, O. Saarikivi, A. Salim, S. Shah, H. S. Behl, X. Wang, S. Bubeck, R. Eldan, A. T. Kalai, Y. T. Lee, and Y. Li · 2023
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Language models can teach themselves to program better
P. Haluptzok, M. Bowers, and A. T. Kalai · 2023
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Mistral 7b, 2023
A. Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D. S. Chaplot, D. de las Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier, L. R. Lavaud, M.-A. Lachaux, P. Stock, T. L. Scao, T. Lavril, T. Wang, T. Lacroix, and W. E. Sayed · 2023
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Impact of code language models on automated program repair
N. Jiang, K. Liu, T. Lutellier, and L. Tan · 2023
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Swe-bench: Can language models resolve real-world github issues?
C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and K. Narasimhan · 2023
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Swe-bench: Can language models resolve real-world github issues?, 2023
C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and K. Narasimhan · 2023
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Inferfix: End-to-end program repair with llms
M. Jin, S. Shahriar, M. Tufano, X. Shi, S. Lu, N. Sundaresan, and A. Svyatkovskiy · 2023
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Openassistant conversations - democratizing large language model alignment
A. Köpf, Y. Kilcher, D. von Rütte, S. Anagnostidis, Z. R. Tam, K. Stevens, A. Barhoum, D. M. Nguyen, O. Stanley, R. Nagyfi, S. ES, S. Suri, D. A. Glushkov, A. V. Dantuluri, A. Maguire, C. Schuhmann, H. Nguyen, and A. J. Mattick · 2023
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Codamosa: Escaping coverage plateaus in test generation with pre-trained large language models
C. Lemieux, J. P. Inala, S. K. Lahiri, and S. Sen · 2023
Cited alongside, same era.
Starcoder: may the source be with you!, 2023
R. Li, L. B. Allal, Y. Zi, N. Muennighoff, D. Kocetkov, C. Mou, M. Marone, C. Akiki, J. Li, J. Chim, Q. Liu, E. Zheltonozhskii, T. Y. Zhuo, T. Wang, O. Dehaene, M. Davaadorj, J. Lamy-Poirier, J. Monteiro, O. Shliazhko, N. Gontier, N. Meade, A. Zebaze, M.-H. Yee, L. K. Umapathi, J. Zhu, B. Lipkin, M. Oblokulov, Z. Wang, R. Murthy, J. Stillerman, S. S. Patel, D. Abulkhanov, M. Zocca, M. Dey, Z. Zhang, N. Fahmy, U. Bhattacharyya, W. Yu, S. Singh, S. Luccioni, P. Villegas, M. Kunakov, F. Zhdanov, M. Romero, T. Lee, N. Timor, J. Ding, C. Schlesinger, H. Schoelkopf, J. Ebert, T. Dao, M. Mishra, A. Gu, J. Robinson, C. J. Anderson, B. Dolan-Gavitt, D. Contractor, S. Reddy, D. Fried, D. Bahdanau, Y. Jernite, C. M. Ferrandis, S. Hughes, T. Wolf, A. Guha, L. von Werra, and H. de Vries · 2023
Cited alongside, same era.
Is your code generated by chatGPT really correct? rigorous evaluation of large language models for code generation
J. Liu, C. S. Xia, Y. Wang, and L. Zhang · 2023
Cited alongside, same era.
Wizardcoder: Empowering code large language models with evol-instruct, 2023
Z. Luo, C. Xu, P. Zhao, Q. Sun, X. Geng, W. Hu, C. Tao, J. Ma, Q. Lin, and D. Jiang · 2023
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Introducing command r+: A scalable llm built for business, April 4 2024
A. Gomez · 2024
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Deepseek-coder: When the large language model meets programming – the rise of code intelligence, 2024
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
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Livecodebench: Holistic and contamination free evaluation of large language models for code
N. Jain, K. Han, A. Gu, W.-D. Li, F. Yan, T. Zhang, S. Wang, A. Solar-Lezama, K. Sen, and I. Stoica · 2024
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Mixtral of experts, 2024
A. Q. Jiang, A. Sablayrolles, A. Roux, A. Mensch, B. Savary, C. Bamford, D. S. Chaplot, D. de las Casas, E. B. Hanna, F. Bressand, G. Lengyel, G. Bour, G. Lample, L. R. Lavaud, L. Saulnier, M.-A. Lachaux, P. Stock, S. Subramanian, S. Yang, S. Antoniak, T. L. Scao, T. Gervet, T. Lavril, T. Wang, T. Lacroix, and W. E. Sayed · 2024
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Self-alignment with instruction backtranslation
X. Li, P. Yu, C. Zhou, T. Schick, O. Levy, L. Zettlemoyer, J. E. Weston, and M. Lewis · 2024
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Octopack: Instruction tuning code large language models, 2023
N. Muennighoff, Q. Liu, A. Zebaze, Q. Zheng, B. Hui, T. Y. Zhuo, S. Singh, X. Tang, L. von Werra, and S. Longpre · 2023
Cited alongside, same era.
Open Source Implementation of Evol-Instruct-Code
nickrosh · 2023
Cited alongside, same era.
Codegen: An open large language model for code with multi-turn program synthesis
E. Nijkamp, B. Pang, H. Hayashi, L. Tu, H. Wang, Y. Zhou, S. Savarese, and C. Xiong · 2023
Cited alongside, same era.
Gpt-4 technical report, 2023
OpenAI · 2023
Cited alongside, same era.
Terms of service, 3 2023
OpenAI · 2023
Cited alongside, same era.
Understanding the effectiveness of large language models in code translation
R. Pan, A. R. Ibrahimzada, R. Krishna, D. Sankar, L. P. Wassi, M. Merler, B. Sobolev, R. Pavuluri, S. Sinha, and R. Jabbarvand · 2023
Cited alongside, same era.
Direct preference optimization: Your language model is secretly a reward model
R. Rafailov, A. Sharma, E. Mitchell, C. D. Manning, S. Ermon, and C. Finn · 2023
Cited alongside, same era.
Code llama: Open foundation models for code, 2023
B. Rozière, J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. E. Tan, Y. Adi, J. Liu, T. Remez, J. Rapin, A. Kozhevnikov, I. Evtimov, J. Bitton, M. Bhatt, C. C. Ferrer, A. Grattafiori, W. Xiong, A. Défossez, J. Copet, F. Azhar, H. Touvron, L. Martin, N. Usunier, T. Scialom, and G. Synnaeve · 2023
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Self-alignment with instruction backtranslation
X. Li, P. Yu, C. Zhou, T. Schick, O. Levy, L. Zettlemoyer, J. E. Weston, and M. Lewis · 2024
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Learning code preference via synthetic evolution
J. Liu, T. Nguyen, M. Shang, H. Ding, X. Li, Y. Yu, V. Kumar, and Z. Wang · 2024
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Large language model-based agents for software engineering: A survey
J. Liu, K. Wang, Y. Chen, X. Peng, Z. Chen, L. Zhang, and Y. Lou · 2024
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Evaluating language models for efficient code generation
J. Liu, S. Xie, J. Wang, Y. Wei, Y. Ding, and L. Zhang · 2024
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Starcoder 2 and the stack v2: The next generation
A. Lozhkov, R. Li, L. B. Allal, F. Cassano, J. Lamy-Poirier, N. Tazi, A. Tang, D. Pykhtar, J. Liu, Y. Wei, et al · 2024
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Large language model guided protocol fuzzing
R. Meng, M. Mirchev, M. Böhme, and A. Roychoudhury · 2024
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Gpt-4o system card
OpenAI · 2024
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Snowflake arctic: The best llm for enterprise ai — efficiently intelligent, truly open, April 24 2024
S. A. Research · 2024
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SALMON: Self-alignment with instructable reward models
Z. Sun, Y. Shen, H. Zhang, Q. Zhou, Z. Chen, D. D. Cox, Y. Yang, and C. Gan · 2024
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Codegemma: Open code models based on gemma, 2024
C. Team, H. Zhao, J. Hui, J. Howland, N. Nguyen, S. Zuo, A. Hu, C. A. Choquette-Choo, J. Shen, J. Kelley, K. Bansal, L. Vilnis, M. Wirth, P. Michel, P. Choy, P. Joshi, R. Kumar, S. Hashmi, S. Agrawal, Z. Gong, J. Fine, T. Warkentin, A. J. Hartman, B. Ni, K. Korevec, K. Schaefer, and S. Huffman · 2024
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Gemini: A family of highly capable multimodal models, 2024
G. Team · 2024
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Code with codeqwen1.5, April 16 2024
Q. Team · 2024
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Openhands: An open platform for ai software developers as generalist agents, 2024
X. Wang, B. Li, Y. Song, F. F. Xu, X. Tang, M. Zhuge, J. Pan, Y. Song, B. Li, J. Singh, H. H. Tran, F. Li, R. Ma, M. Zheng, B. Qian, Y. Shao, N. Muennighoff, Y. Zhang, B. Hui, J. Lin, R. Brennan, H. Peng, H. Ji, and G. Neubig · 2024
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Arctic-snowcoder: Demystifying high-quality data in code pretraining
Y. Wei, H. Han, and R. Samdani · 2024
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M. Weyssow, A. Kamanda, and H. Sahraoui · 2024
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Agentless: Demystifying llm-based software engineering agents
C. S. Xia, Y. Deng, S. Dunn, and L. Zhang · 2024
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Top leaderboard ranking= top coding proficiency, always? evoeval: Evolving coding benchmarks via llm
C. S. Xia, Y. Deng, and L. Zhang · 2024
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Swe-agent: Agent-computer interfaces enable automated software engineering, 2024
J. Yang, C. E. Jimenez, A. Wettig, K. Lieret, S. Yao, K. Narasimhan, and O. Press · 2024
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Wavecoder: Widespread and versatile enhanced instruction tuning with refined data generation, 2024
Z. Yu, X. Zhang, N. Shang, Y. Huang, C. Xu, Y. Zhao, W. Hu, and Q. Yin · 2024
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Self-rewarding language models, 2024
W. Yuan, R. Y. Pang, K. Cho, X. Li, S. Sukhbaatar, J. Xu, and J. Weston · 2024
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Opencodeinterpreter: Integrating code generation with execution and refinement
T. Zheng, G. Zhang, T. Shen, X. Liu, B. Y. Lin, J. Fu, W. Chen, and X. Yue · 2024
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Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions, 2024
T. Y. Zhuo, M. C. Vu, J. Chim, H. Hu, W. Yu, R. Widyasari, I. N. B. Yusuf, H. Zhan, J. He, I. Paul, S. Brunner, C. Gong, T. Hoang, A. R. Zebaze, X. Hong, W.-D. Li, J. Kaddour, M. Xu, Z. Zhang, P. Yadav, N. Jain, A. Gu, Z. Cheng, J. Liu, Q. Liu, Z. Wang, D. Lo, B. Hui, N. Muennighoff, D. Fried, X. Du, H. de Vries, and L. V. Werra · 2024
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