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Automating code documentation through explanatory text can prove highly beneficial in code understanding.
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Evaluating source code summarization techniques: Replication and expansion,
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Automatic generation of natural language summaries for java classes,
L. Moreno, J. Aponte, G. Sridhara, A. Marcus, L. Pollock, K. Vijay-Shanker, · 2013
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Automatic generation of text descriptive comments for code blocks,
Y. Liang, K. Zhu, · 2018
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Deep code comment generation,
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Codebert: A pre-trained model for programming and natural languages,
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Lamner: code comment generation using character language model and named entity recognition,
R. Sharma, F. Chen, F. Fard, · 2022
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Few-shot training llms for project-specific code-summarization,
T. Ahmed, P. Devanbu, · 2022
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8-bit optimizers via block-wise quantization,
T. Dettmers, M. Lewis, S. Shleifer, L. Zettlemoyer, · 2022
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S. Mangrulkar, S. Gugger, L. Debut, Y. Belkada, S. Paul, B. Bossan, Peft: State-of-the-art parameter-efficient fine-tuning methods, https://github.com/huggingface/peft , 2022
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Application of large language models to software engineering tasks: Opportunities, risks, and implications,
I. Ozkaya, · 2023
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J. Jiang, S. Kim, Codeup: A multilingual code generation llama2 model with parameter-efficient instruction-tuning, https://huggingface.co/deepse , 2023
2023
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Natural language generation and understanding of big code for AI-assisted programming: A review,
M.-F. Wong, S. Guo, C.-N. Hang, S.-W. Ho, C.-W. Tan, · 2023
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Experiences from using code explanations generated by large language models in a web software development e-book,
S. MacNeil, A. Tran, A. Hellas, J. Kim, S. Sarsa, P. Denny, S. Bernstein, J. Leinonen, · 2023
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Harnessing the power of llms in practice: A survey on chatgpt and beyond,
J. Yang, H. Jin, R. Tang, X. Han, Q. Feng, H. Jiang, B. Yin, X. Hu, · 2023
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Alpaca: A strong, replicable instruction-following model,
R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, T. B. Hashimoto, · 2023
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G. Penedo, Q. Malartic, D. Hesslow, R. Cojocaru, A. Cappelli, H. Alobeidli, B. Pannier, E. Almazrouei, J. Launay, · 2023
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2023
Cited alongside, same era.
Llama 2: Open foundation and fine-tuned chat models,
H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, et al., · 2023
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Starcoder: may the source be with you!,
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Generative ai for software metadata: Overview of the information retrieval in software engineering track at fire 2023,
S. Majumdar, S. Paul, D. Paul, A. Bandyopadhyay, B. Dave, S. Chattopadhyay, P. P. Das, P. D. Clough, P. Majumder, · 2023
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B. Rozière, J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. E. Tan, Y. Adi, J. Liu, T. Remez, J. Rapin, et al., · 2023
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Manuel Romero, llama-2-coder-7b (revision d30d193), 2023. URL: https://huggingface.co/mrm8488/llama-2-coder-7b . doi: 10.57967/hf/0931
2023
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2023
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Large language models are few-shot summarizers: Multi-intent comment generation via in-context learning (2024)
M. Geng, S. Wang, D. Dong, H. Wang, G. Li, Z. Jin, X. Mao, X. Liao, · 2024
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Summarizing source code using a neural attention model,
S. Iyer, I. Konstas, A. Cheung, L. Zettlemoyer, · 2083
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