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English Information

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Authors
# Name
1 Paula Woyames(pnwoyames@id.uff.br)
2 Débora Pina(dbpina@cos.ufrj.br)
3 Liliane Kunstmann(lneves@cos.ufrj.br)
4 Marta Mattoso(marta@cos.ufrj.br)
5 Daniel de Oliveira(danielcmo@ic.uff.br)

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Reference
# Reference
1 Babuji, Y., Woodard, A., Li, Z., Katz, D. S., Clifford, B., Kumar, R., Lacinski, L., Chard, R., Wozniak, J. M. , Foster, I., Wilde, M., and Chard, K. 2019. Parsl: Pervasive Parallel Programming in Python. In Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing (HPDC '19). Association for Computing Machinery, New York, NY, USA, 25–36. https://doi.org/10.1145/3307681.3325400
2 Choi, Hyeong Kyu, and Yixuan Li. "Picle: Eliciting diverse behaviors from large language models with persona in-context learning." arXiv preprint arXiv:2405.02501 (2024).
3 de Oliveira, Daniel, Ji Liu, and Esther Pacitti. Data-Intensive Workflow Management. Springer International Publishing, 2019.
4 Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature biotechnology, 35(4), 316-319.
5 Dong, Qingxiu, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, Lei Li and Zhifang Sui. “A Survey on In-context Learning.” Conference on Empirical Methods in Natural Language Processing (2022).
6 Duque, A., Syed, A., Day, K. V., Berry, M. J., Katz, D. S., & Kindratenko, V. V. (2023). Leveraging large language models to build and execute computational workflows. arXiv preprint arXiv:2312.07711.
7 Koziolek, H., Grüner, S., Hark, R., Ashiwal, V., Linsbauer, S., & Eskandani, N. (2024, April). LLM-based and retrieval-augmented control code generation. In Proceedings of the 1st International Workshop on Large Language Models for Code (pp. 22-29).
8 Rocklin, Matthew. "Dask: Parallel computation with blocked algorithms and task scheduling." SciPy. 2015.
9 Paiva, L., Assis, G., Amorim, A., Dias, L. G., Paes, A., Oliveira, D. . (2025). Domínio delimitado, Ódio exposto: O uso de prompts para identificação de discurso de Ódio online com LLMs. In SBBD’25, Fortaleza, Brasil
10 Sänger, M., De Mecquenem, N., Lewińska, K. E., Bountris, V., Lehmann, F., Leser, U., & Kosch, T. (2024). A qualitative assessment of using ChatGPT as large language model for scientific workflow development. GigaScience, 13, giae030.
11 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
12 Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V. & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35, 24824-24837.
13 Xu, J., Du, W., Liu, X., & Li, X. (2024, October). Llm4workflow: An llm-based automated workflow model generation tool. In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering (pp. 2394-2398).
14 Yildiz, O., & Peterka, T. (2024). Do Large Language Models Speak Scientific Workflows?. arXiv preprint arXiv:2412.10606.
15 Zhang, X., Xie, Y., Huang, J., Ma, J., Pan, Z., Liu, Q., Xiong, Z., Ergen, T., Shim, D., Lee, H., & Mei, Q. (2024). Massw: A new dataset and benchmark tasks for ai-assisted scientific workflows. arXiv preprint arXiv:2406.06357.